当前位置:文档之家› Subjective Bayesian Analysis Principles and Practice

Subjective Bayesian Analysis Principles and Practice

Subjective Bayesian Analysis Principles and Practice
Subjective Bayesian Analysis Principles and Practice

Bayesian Analysis(2006)1,Number3,pp.403–420

Subjective Bayesian Analysis:Principles and

Practice

Michael Goldstein?

Abstract.We address the position of subjectivism within Bayesian statistics.

We argue,?rst,that the subjectivist Bayes approach is the only feasible method

for tackling many important practical problems.Second,we describe the essential

role of the subjectivist approach in scienti?c analysis.Third,we consider possible

modi?cations to the Bayesian approach from a subjectivist viewpoint.Finally,we

address the issue of pragmatism in implementing the subjectivist approach.

Keywords:coherency,exchangeability,physical model analysis,high reliability

testing,objective Bayes,temporal sure preference

1Introduction

The subjective Bayesian approach is based on a very simple collection of ideas.You are uncertain about many things in the world.You can quantify your uncertainties as probabilities,for the quantities you are interested in,and conditional probabilities for observations you might make given the things you are interested in.When data arrives, Bayes theorem tells you how to move from your prior probabilities to new conditional probabilities for the quantities of interest.If you need to make decisions,then you may also specify a utility function,given which your preferred decision is that which maximises expected utility with respect to your conditional probability distribution.

There are many compelling accounts explaining how and why this view should form the basis for statistical methodology;see,for example,Lindley(2000)and the accompa-nying discussion.Careful treatments of the Bayesian approach are given in,for example, Bernardo and Smith(1994),O’Hagan and Forster(2004)and Robert(2001).In partic-ular,Lad(1996)provides an excellent introduction to the subjectivist viewpoint,with a wide ranging collection of references to the development of this position.

Moving from principles to practice can prove very challenging and so there are many ?avours of Bayesianism re?ecting the technical challenges and requirements of di?erent ?elds.In particular,a form of Bayesian statistics,termed“objective Bayes”aims to gain the formal advantages arising from the structural clarity of the Bayesian approach without paying the“price”of introducing subjectivity into statistical analysis.Such attempts raise important questions as to the role of subjectivism in Bayesian statistics. This account is my subjective take on the issue of subjectivism.

My treatment is split into four parts.First,the subjectivist Bayes approach is the only feasible method for tackling many important practical problems,and in Section ?Department of Mathematical Sciences,University of Durham,UK, https://www.doczj.com/doc/3d2061769.html,:8000/stats/people/mg/mg.html

c 2006International Society for Bayesian Analysis ba0003

404Subjective Bayesian Analysis 2I’ll give examples to illustrate this.Next,in Section3,I’ll look at scienti?c analy-ses,where the role of subjectivity is more controversial,and argue the necessity of the subjective formulation in this context.In Section4,I’ll consider how well the Bayes approach stands up to scrutiny from the subjective viewpoint itself.In Section5,I’ll dis-cuss the issue of pragmatism in implementing the subjectivist approach.In conclusion, I’ll comment on general implications for developing the full potential of the subjectivist approach to Bayesian analysis.

2Applied subjectivism

Among the most important growth areas for Bayesian methodology are those applica-tions that are so complicated that there is no obvious way even to formulate a more traditional analysis.Such applications are widespread;for many examples,consult the series of Bayesian case studies volumes from the Carnegie Mellon conference series. Here are just a couple of areas that I have been personally involved in,with colleagues at Durham,chosen so that I can discuss,from the inside,the central role played by subjectivity.

2.1High reliability testing for complex systems

Suppose that we want to test some very complicated system-a large software system would be a good example of this.Software testing is a crucial component of the software creation cycle,employing large numbers of people and consuming much of the software budget.However,while there is a great deal of practical expertise in the software testing community,there is little rigorous guidance for the basic questions of software testing, namely how much testing a system needs,and how to design an e?cient test suite for this purpose.Though the number of tests that we could,in principle,carry out is enormous,each test has non-trivial costs,both in time and money,and we must plan testing(and retesting given each fault we uncover)to a tight time/money budget.How can we design and analyse an optimal test suite for the system?

This is an obvious example of a Bayesian application waiting to happen.There is enormous uncertainty and we are forced to extrapolate beliefs about the results of all the tests that we have not carried out from the outcomes of the relatively small number of tests that we do carry out.There is a considerable amount of prior knowledge carried by the testers who are familiar with the ways in which this software is likely to fail,both from general considerations and from testing and?eld reports for earlier generations of the software.The expertise of the testers therefore lies in the informed nature of the prior beliefs that they hold.However,this expertise does not extend to an ability to analyse,without any formal support tools,the conditional e?ect of test observations on their prior beliefs,still less to an ability to design a test system to extract optimum information from this extremely complex and interconnected probabilistic system.

A Bayesian approach proceeds as follows.First,we construct a Bayesian belief net. In this net,the ancestor nodes represent the various general reasons that the testers

Michael Goldstein405 may attribute to software failure,for example incorrectly stripping leading zeroes from a number.The links between ancestor nodes show relationships between these types of failure.The child nodes are the various test types,where the structuring ensures that all tests represented by a given test node are regarded exchangeably by the testers.Second, we quantify beliefs as to the likely levels of failure of each type and the conditional e?ects of each failure type on each category of test outcome.Finally,we may choose a test suite to optimise any prespeci?ed criterion,either based on the probability of any faults remaining in the system or on the utility of allowing certain types of failure to pass undetected at software release.This optimality calculation is tractable even for large systems.This is because what concerns us,for any test suite,is the probability of faults remaining given that all the chosen tests are successful,provided any faults that are detected will be?xed before release.

In principle,this methodology,by combining Bayesian belief networks with optimal experimental design,is massively more e?cient and?exible than current approaches. Is the approach practical?From our experiences working with an industrial partner,I would say de?nitely yes.A general overview of the approach that we developed is given in Woo?et al.(2002).As an indication of the potential increase in e?ciency,we found, in one case study,that Bayesian automatic design provided eight tests which together were more e?cient than233tests designed by the original testing team,and identi?ed additional tests that were appropriate for checking areas of functionality that had not been covered by the original test suite.This is not a criticism of the testers,who were very experienced,but simply illustrates that optimal multi-factor probabilistic design is very di?cult.The value of the subjectivist approach lies in translating the compli-cated but informal generalised uncertainty judgements of the experts into a language which allows for precise and rigorous analysis.In system testing,the careful use of this language o?ers enormous potential for clarity and e?ciency gains.

Of course,there are many issues that must be sorted out before such bene?ts can be realised,from the construction of user-friendly interfaces for building the models to(a much larger obstacle!)the culture change required to recognise and routinely exploit such methods.However,the subjective Bayes approach does provide a complete framework for quantifying and managing the uncertainties of high-reliability testing.It is hard to imagine any other approach which could do so.

2.2Complex physical systems

Many large physical systems are studied through a combination of mathematical mod-elling,computer simulation and matching against past physical data,which can,hope-fully,be used to extrapolate future system behaviour;for example,this accounts for much of what we claim to know about the nature and pace of global climate change. Such analysis is riddled with uncertainty.In climate modelling,each computer simu-lation can take between days and months,and requires many input parameters to be set,whose values are unknown.Therefore,we may view computer simulations with varied choices of input parameters as a small sample of evaluations from a very high dimensional unknown function.The only way to learn about the input parameters is

406Subjective Bayesian Analysis by matching simulator output to historical data,which is,itself,observed with error. Finally,and often most important,the computer simulator is just a model,and we need to consider the ways in which the model and reality may di?er.

Again,the subjectivist Bayesian approach o?ers a framework for specifying and synthesising all of the uncertainties in the problem.There is a wide literature on the probabilistic treatment of computer models;a good starting point with a wide collec-tion of references is the recent volume Santner et al.(2003).Our experience at Durham started with work on oil reservoir simulators,which are constructed to help with all the problems involved in e?cient management of reservoirs.Typically,these are very high dimensional computer models which are very slow to evaluate.The approach that we employed for reservoir uncertainty analysis was based on representing the reservoir sim-ulator by an emulator.This is a probabilistic description of our beliefs about the value of the simulator at each input value.This is combined with statements of uncertainty about the input values,about the discrepancy between the model and the reservoir and about the measurement uncertainty associated with the historical data.This completely speci?ed stochastic system provides a formal framework allowing us to synthesise expert elicitation,historical data and a careful choice of simulator runs.While there are many challenging technical issues arising from the size and complexity of the system,this spec-i?cation does allow us to identify“correct”settings for simulator inputs(often termed history matching in the oil industry),see Craig et al.(1996),and to assess uncertainty for forecasts of future behaviour of the physical system,see Craig et al.(2001).Our approach relies on a Bayes linear foundation(which I’ll discuss in Section4)to handle the technical di?culties involved with the high dimensional analysis;for a full Bayes approach for related problems,see Kennedy and O’Hagan(2001).

Our approach has been implemented in software employed by users in the oil indus-try,through our collaborators ESL(Energy SciTech Limited).This means that we get to keep track,just a little,of how the approach works in practice.Here’s an example of the type of success which ESL has reported to us.They were asked to match an oil?eld containing650wells,based on one million plus grid cells(for each of which permeability,porosity,fault lines,etc.are unknown inputs).Finding the previous best history match had taken one man-year of e?ort.Our Bayesian approach,starting from scratch,found a match using32runs(each lasting4hours and automatically chosen by the software),with a fourfold improvement according to the oil company measure of match quality.This kind of performance is impressive,although,of course,these remain very hard problems and much must still be done to make the approach more ?exible,tractable and reliable.

Applications such as these make it clear that careful representation of subjective beliefs can give much improved performance in tasks that people are already trying to do.There is an enormous territory where subjective Bayes methods are the only feasible way forward.This is not to discount the large amount of work that must often be done to bring an application into Bayes form,but simply to observe that for such applications there are no real alternatives.In such cases,the bene?ts from the Bayesian formulation are potentially very great and clearly demonstrable.The only remaining issue,therefore,is whether such bene?ts outweigh the e?orts required to achieve them.

Michael Goldstein407 This“pain to gain”ratio is crucial to the success of subjective Bayes applications.When the answer really matters,such as for global climate change,the pain threshold would have to be very high indeed to dissuade us from the analysis.

By explicitly introducing our uncertainty about the ways in which our models fall short of reality,the subjective Bayes analysis also does something new and important. Only technical experts are concerned with how climate models behave,while everybody has an interest in how global climate will actually change.For example,the Guardian newspaper leader on Burying Carbon(Feb3,2005)tell us that“the chances of the Gulf Stream-the Atlantic thermohaline circulation that keeps Britain warm-shutting down are now thought to be greater than50%.”This sounds like something we should know.However,I am reasonably con?dent that no climate scientist has actually carried an uncertainty analysis which would be su?cient to provide a logical bedrock for such a statement.We can only use the analysis of a global climate model to guide rational policy towards climate change if we can construct a statement of uncertainty about the relation between analysis from the climate model and the behaviour of the real climate. To further complicate the assessment,there are many models for climate change in current use,all of whose analyses should be synthesised as the basis of any judgements about actual climate change.Specifying beliefs about the discrepancy between models and reality is unfamiliar and di?cult.However,we cannot avoid this task if we want our statements to carry weight in the real world.A general framework for making such speci?cations is described in Goldstein and Rougier(2005).

3Scienti?c subjectivism

3.1The role of subjectivism in scienti?c enquiry

In the kind of applications we’ve discussed so far,the only serious issues about the role of subjectivity are pragmatic ones.Each aspect of the speci?cation,whether part of the“likelihood function”or the“prior distribution,”encodes a collection of subjective judgements.The value of the Bayesian approach lies?rst in providing a language within which we can express all these judgements and second in providing a calculus for analysing these judgements.

Controversy over the role of subjectivity tends to occur in those areas of scienti?c experimentation where we do appear to have a greater choice of statistical approaches. Laying aside the obvious consideration that any choice of analysis is the result of a host of subjective choices,there are,essentially,two types of objections to the explicit use of subjective judgements;those of principle,namely that subjective judgements have no place in scienti?c analyses;and those of practice,namely that the pain to gain ratio is just too high.

These are deep issues which have received much attention;a good starting place for discussion of the role of Bayesian analysis in traditional science is Howson and Urbach (1989).Much of the argument can be captured in simple examples.Here’s one such, versions of which are often used to introduce the Bayesian idea to people who already

408Subjective Bayesian Analysis have some familiarity with traditional statistical analysis.

First,we can imagine carrying out Fisher’s famous tea-tasting experiment.Here an individual,Joan say,claims to be able to tell whether the milk or the tea has been added?rst in a cup of tea.We perform the experiment of preparing ten cups of tea, choosing each time on a coin?ip whether to add the milk or tea?rst.Joan then tastes each cup and gives an opinion as to which ingredient was added?rst.We count the number,X,of correct assessments.Suppose,for example,that X=9.

Now compare the tea-tasting experiment to an experiment where an individual, Harry say,claims to have ESP as demonstrated by being able to forecast the outcome of fair coin?ips.We test Harry by getting forecasts for ten?ips.Let X be the number of correct forecasts.Suppose that,again,X=9.

Within the traditional view of statistics,we might accept the same formalism for the two experiments,namely that,for each experiment,each assessment is independent with probability p of success.In each case,X has a binomial distribution parameters10 and p,where p=1/2corresponds to pure guessing.Within the traditional approach, the likelihood is the same,the point null is the same if we carry out a test for whether p=1/2,and con?dence intervals for p will be the same.

However,even without carrying out formal calculations,I would be fairly convinced of Joan’s tea tasting powers while remaining unconvinced that Harry has ESP.You might decide di?erently,but that is because you might make di?erent prior judgements. This is what the Bayesian approach adds.First,we require our prior probability,g say,that Harry or Joan is guessing.Then,if not guessing,we need to specify a prior distribution q over possible values of p.Given g,q,we can use Bayes theorem to update our probability that Harry or Joan is just guessing and,if not guessing,we can update our prior distribution over p.We may further clarify the Bayesian account by giving a more careful description of our uncertainty within each experiment based on our judgements of exchangeability for the individual outcomes.This allows us to replace our judgements about the abstract model parameter p with judgements about observable experimental outcomes as the basis for the analysis.

Therefore,the Bayes approach shows us exactly how and where to input our prior judgements.We have moved away from a traditional view of a statistical analysis, which attempts to express what we may learn about some aspect of reality by analysing an individual data set.Instead,the Bayesian analysis expresses our current state of belief based on combining information from the data in question with whatever other knowledge we consider relevant.

The ESP experiment is particularly revealing for this discussion.I used to use it routinely for teaching purposes,considering that it was su?ciently unlikely that Harry would actually possess ESP that the comparison with the tea-tasting experiment would be self-evident.I eventually came to realise that some of my students considered it perfectly reasonable that Harry might possess such powers.While writing this article, I tried googling“belief in ESP”over the net,which makes for some intriguing reading. Here’s a particularly relevant discussion from an article in the September2002issue of

Michael Goldstein409 Scienti?c American,by Michael Sherme,titled“Smart People Believe Weird Things”. After noting that,for example,around60%of college graduates appear to believe in ESP,Sherme reports the results of a study that found“no correlation between science knowledge(facts about the world)and paranormal beliefs.”The authors,W.Richard Walker,Steven J.Hoekstra and Rodney J.Vogl,concluded:“Students that scored well on these[science knowledge]tests were no more or less sceptical of pseudo-scienti?c claims than students that scored very poorly.Apparently,the students were not able to apply their scienti?c knowledge to evaluate these pseudo-scienti?c claims.We suggest that this inability stems in part from the way that science is traditionally presented to students:Students are taught what to think but not how to think.”Sherme continues as follows:“To attenuate these paranormal belief statistics,we need to teach that science is not a database of unconnected factoids but a set of methods designed to describe and interpret phenomena,past or present,aimed at building a testable body of knowledge open to rejection or con?rmation.”

The subjective Bayesian approach may be viewed as a formal method for connecting experimental factoids.Rather than treating each data set as though it has no wider context,and carrying out each statistical analysis just as though this were the?rst investigation that had ever been carried out of any relevance to the questions at issue, we consider instead how the data in question adds to,or changes,our beliefs about these questions.

If we think about the ESP experiment in this way,then we should expand the prob-lem description to re?ect this requirement.Here is a minimum that I should consider. First,I would need to assess my probability for E,the event that ESP is a real phe-nomenon that at least some people possess.This is the event that joins my analysis of Harry’s performance with my generalised knowledge of the scienti?c phenomenon at issue.Conditional on E,I should evaluate my probability for J,the event that Harry possesses ESP.Conditional on J and on J complement,I should evaluate my proba-bilities for G,the event that Harry is just guessing and C,the event that either the experiment is?awed or Harry is,somehow,cheating;for example,the coin might be heads biased and Harry mostly calls heads.This is the event that captures my gen-eralised knowledge of the reliability of experimental procedures in this area.If there is either cheating or ESP,I need a probability distribution over the magnitude of the e?ect.

What do we achieve by this formalism?First,this gives me a way of assessing my actual posterior probability for whether Harry has ESP.Second,if I can lay out the considerations that I use in a transparent way,it is easy for you to see how your conclusions might di?er from mine.If we disagree as to whether Harry has ESP,then we can trace this disagreement back to di?ering probabilities for the general phenomenon, in this case ESP,or di?erent judgements about particulars of the experiment,such as Harry’s possible ability at sleight of hand.More generally,by considering the range of prior judgements that might reasonably be made,I can distinguish between the extent to which the experiment might convince me as to Harry’s ESP,and the e?ect it might have on others.I could even determine how large and how stringently controlled an experiment would need to be in order to have a chance of convincing me of Harry’s

410Subjective Bayesian Analysis powers.More generally,how large would the experiment need to be to convince the wider community?

The above example provides a simple version of a general template for any scienti?c Bayesian analysis.There are scienti?c questions at issue.Beliefs about these issues require prior speci?cation.Then we must consider the relevance of the scienti?c for-mulation to the current experiment along with all the possible?aws in the experiment which would invalidate the analysis.Finally,a likelihood must be speci?ed,expressing data variability given the hypotheses of interest.

There are two versions of the subsequent analysis.First,you may only want to know how to revise your own beliefs given the data.Such private analyses are quite common. Many scientists carry out at least a rough Bayes assessment of their results,even if they never make such analysis public.

Second,you may wish to publish your results,to contribute to,or even to settle, a scienti?c issue.It may be that you can construct a prior speci?cation that is very widely supported.Alternately,it may be that,as with the ESP experiment,no such generally agreed prior speci?cation may be made.Indeed,the disagreement between experts may be precisely what the experiment is attempting to resolve.Therefore,our Bayesian analysis of an experiment should begin with a probabilistic description whose qualitative form can be agreed on by everyone.This means that all features,in the prior and the likelihood,that cause substantial disagreement should have explicit form in the representation,so that di?ering judgements can be expressed over them.There is a rich literature on elicitation,dealing with how generalised expert knowledge may be converted into probabilistic form;for a recent overview,see Garthwaite et al.(2005). As with each other aspect of the scienti?c argument,such elicitation has two aims;?rst,to obtain sensible prior values and second,to make clear the scienti?c basis for assigning these values.Statistical aspects of the representation may employ standard data sharing methodologies such as meta-analysis,multi-level modelling and empirical Bayes,provided all the relevant judgements are well sourced.We can then produce the range of posterior judgements,given the data,which correspond to the range of “reasonable”prior judgements held within the scienti?c community.We may argue that a scienti?c case is“proven”if the evidence should be convincing given any reasonable assignment of prior beliefs.Otherwise,we can assess the extent to which the community might still di?er given the evidence.We should make this analysis at the planning stage in order to design experiments that can be decisive for the scienti?c community or to conclude that no such experiments are feasible.

All of this is clear in principle,though implementation of the program may be di?cult in individual cases.Each uncertainty statement is a well sourced statement of belief by an individual.If individual judgements di?er and if this is relevant,then such di?erences are re?ected in the analysis.In practice,it is unusual to?nd such a subjectivist approach within scienti?c analysis.Let us therefore consider objections and alternatives to the subjective Bayesian approach.

Michael Goldstein411 3.2Objections and alternatives to scienti?c subjectivism

The principled objection to Bayesian subjectivism is that the subjective Bayesian ap-proach answers problems wrongly,because of unnecessary and unhelpful appeals to arbitrary prior assumptions,which should have no place in scienti?c analyses.Individ-ual subjective reasoning is inappropriate for reaching objective scienti?c conclusions, which form the basis of consensus within the scienti?c community.

This objection would have more force if there was a logically acceptable alternative.I do not here want to dwell on the di?culties in interpretation of the core concepts of more traditional inference,such as signi?cance and coverage properties:a valid con?dence interval may be empty,for example when constructed by the intersection of a series of repeated con?dence intervals;a statistically signi?cant result obtained with high power may be almost certainly false,and so forth.Further,I do not know of any way to construct even the basic building blocks of the inference,such as the relative frequency probabilities that we must use if we reject the subjective interpretation,that will stand up to proper logical scrutiny.Instead,let us address the principled objection directly. We cannot consider whether the Bayes approach is appropriate without?rst clarifying the objectives of the analysis.When we discussed the analysis of physical models,we made the fundamental distinction between analysis of the model and analysis of the physical system.Analysing various models may give us insights but at some point these insights must be integrated into statements of uncertainty about the system itself. Analysing experimental data is essentially the same.We must be clear as to whether we are analysing the experiment or the problem.

In the ESP experiment,the question is whether Harry has ESP,or,possibly,whether ESP exists at all.If we analyse the experimental data as part of a wider e?ort to address our uncertainty about these questions,then external judgements are clearly relevant. As described above,the beliefs that are analysed may be those of an individual,if that individual can make a compelling argument for the rationality of a particular belief spec-i?cation,or instead we may analyse the collection of beliefs held by informed individuals in the community.The Bayes analysis is appropriate for this task,as it is concerned to evaluate the relevant kinds of uncertainty judgements,namely the uncertainties over the quantities that we want to learn about,given the quantities that we observe,based on careful foundational arguments using ideas such as coherence and exchangeability to show why this is the unavoidable way to analyse our actual uncertainties.

On the other hand,suppose that,for now,we only want to analyse the data from this individual experiment.Our goal,therefore,cannot be to consider directly the basic question about the existence of ESP.Indeed,it is hard to say exactly what our goal is,which is why there often is so much confusion in discussions between proponents of di?erent approaches.All that we can say informally is that the purpose of such analysis is to provide information which will be helpful at some future time for whoever does attempt to address the real questions of interest.We are now in the same position as the modeller;we have great freedom in carrying out our analyses but we must be modest in the claims that we make for them.

412Subjective Bayesian Analysis This is the world in which we?nd objective Bayes methodology.What does the word“objective”mean in this context?It does not mean that there is an objective status for the statements made by the methodology,as the approach doesn’t o?er any other testable meaning for probability statements beyond the uncertainty judgements of the individual.Nor does it mean that there is some objectively testable property that the answers derived by the analysis will necessarily satisfy.Thus,we have no way to judge in what sense,and to what degree,we should have con?dence in the conclusions of an objective Bayes analysis.It does not even mean that there is some objectively testable principle that has been used to assemble the ingredients of the analysis.

Instead,as with many other uses of the term,objective here usually means that we are not attempting to address the question at issue(should we think that Harry has ESP?)but instead we are constructing a model for the inference by introducing and attempting to answer some surrogate question which is less challenging.I’m not sure what the question would be here-perhaps we imagine somebody who has just arrived on this planet and we wonder what our stranger would conclude if immediately confronted by Harry’s performance.Of course,if we formulate this surrogate question too precisely then we will not be able to answer it;after all,we have no idea what such a stranger would actually conclude.This ambiguity can sometimes be benign.If we have a very large experiment,then a simple automatic choice of prior,along with some large sample approximation argument to show that the conclusion is not overly sensitive to the choice of prior,may save a lot of time and e?ort as compared to a full subjectivist analysis while reaching substantially the same conclusion.However,the value of such an analysis still lies in the robust approximation to the full subjectivist analysis.

When analysis of the current experiment is sensitive to the prior speci?cation,as in our ESP experiment,it is clear that there is no objective answer to the question of Harry’s powers,based on analysis of the given data.To pretend otherwise is to enter the world of pseudo-science to which we alluded above,in which we behave just as those science students who appear unable to make the links between reason,experience and observation.Subjective Bayesian analysis is hard but necessary precisely because it does concern such a fully rounded assessment.

There are two dangers as we move away from the subjective Bayes analysis.First, we may calculate results in which we have no con?dence.Second,we may be unsure what our analysis even claims to represent.And if statisticians risk confusion as to the meaning of their analyses,how much greater is the danger for those non statisticians who rely on the output of statistical analyses?As a current and tragic example,the General Medical Council for the UK has just ruled that Professor Sir Roy Meadow should be struck o?the medical register due to serious professional misconduct for giving evidence beyond his expertise at a trial which led to a mother being wrongfully jailed for the murder of her two baby sons.Much of the evidence of misconduct is based around Prof Meadow’s statement at the trial that there was just a“one in73million”chance that two babies with the given background could each su?er cot death.(This?gure was apparently obtained by squaring the circa8,500to one chance of a single baby dying of cot death in a family.)The actual odds are now considered to be far less extreme.The GMC?tness to practise panel said in its verdict that Prof Meadow had failed in his

Michael Goldstein413 duty to check the validity of his statistics.There are many features of the case which are worthy of comment.Of particular relevance is Prof.Meadows defence of his claim. The following comes from the Guardian,July2nd,2005.

“Prof Meadow,whose evidence was used in the cases of three other women wrongly accused of killing their babies,said he had been quoting the statistic from a highly respected report on sudden infant deaths,which at the time had yet to be published. Defending his right to use the report in his evidence at Mrs Clark’s trial,he said,“I was quoting what I believed to be a very thorough study...by experts,several of whom I knew and respected.”Nicola Davies QC,representing Prof Meadow,asked:“Did you have any di?culty with quoting statistics from the study?”He replied:“To me it was like I was quoting a radiologist’s report or a piece of pathology...I was quoting the statistics,I wasn’t pretending to be a statistician.”

I have not seen the study in question,although I have read claims that Prof Meadow quoted some calculations from the study which were taken out of context,ignoring the conditions and quali?cations around the quoted values.However,the general attitude displayed to the statistical analysis,conferring on it a purely objective and value-free status,surely lies at the heart of the issue.Prof Meadow’s professional misfortune may only have been that the statistical‘mistake’for which he is blamed was su?ciently elementary that it could easily be argued that overlooking the error was professionally negligent.I can easily envisage a more sophisticated treatment,say an‘objective Bayes’analysis,which,by placing‘uninformative’priors on certain key parameters in a more elaborate version of the model,could make essentially the same‘error’but in a way which would be far harder to detect.As statistical analyses become more sophisticated and more di?cult for anyone but an expert statistician to check,it becomes increasingly important that the meaning of the statistical analysis is clearly conveyed.Any statisti-cian who does a Bayesian analysis for a problem with important practical consequences but does not make good and clear use of informed judgements,and then labels that analysis as‘objective’,should be aware of the misunderstandings and mistakes that will follow when their claim is taken precisely at face value.

4Pure subjectivism

We have argued that the subjective Bayes approach is successful in practice,and is invaluable for serious scienti?c analysis.This,however,leaves open possible criticisms of the Bayes approach from the subjective viewpoint itself.Carrying out a careful Bayesian analysis can prove very di?cult.In part this is because such an analysis requires an extremely detailed level of prior probabilistic belief speci?cation.Typically, we?nd it di?cult to make detailed speci?cations in a way which genuinely corresponds to our prior beliefs.Therefore,arti?cial,conventional prior forms are used,often bearing only a limited relation to our prior beliefs,so that the resulting Bayesian analysis bears only a limited relation to our actual judgements.

Is such a detailed speci?cation really a necessary consequence of the subjectivist approach?No,I believe that this is a misunderstanding of the full subjectivist position,

414Subjective Bayesian Analysis which I will now brie?y consider.

4.1Conditional and posterior probability

A true subjectivist formulation should start by recognising the limited abilities of the individual to make large collections of uncertainty speci?cations.It is precisely this consideration that led de Finetti,in de Finetti(1974),the most careful statement of the subjectivist position yet written,to chose expectation rather than probability as his primitive for the subjectivist theory.With expectation as primitive,we can assess directly whatever sub-collection of probabilities and expectations we consider ourselves able to specify at any given time,whereas,if probability is the primitive,then we must specify every probability before we can specify any expectation.Unfortunately, the liberating aspect of this approach is somewhat lost in de Finetti’s development,as changes in beliefs are still carried out by conditioning on events,which again requires a ?nely detailed level of prior speci?cation(although we may give bounds on the coherent inferences consistent with any partial speci?cation,see Lad(1996)).

Therefore,we must also consider whether it is an intrinsic part of the subjectivist position that beliefs should change by conditioning via Bayes theorem.This question cuts to the heart of the Bayes position,as it is impossible to demonstrate any de?nitive sense in which beliefs should change by conditioning.You might think that someone, somewhere has proved that conditioning is the correct way to modify beliefs,at least under certain conditions.However,all that can be proved is results such as the follow-ing.Suppose that you specify a joint probability distribution for a collection of random quantities.Suppose that you also write down a rule for changing your probabilities for some of the quantities,as a function of the numerical values of the remaining quantities. If this rule for changing your probabilities is not the usual conditional probability for-mula,then you can be made a sure loser,in the usual sense of placing a sequence of bets that pay o?depending on various combinations of outcomes of the random quantities.

This is not a demonstration that beliefs should change by conditioning:all that it does is to eliminate non-Bayesian rules for updating beliefs in the class of rules based exclusively on current beliefs and the values of the observables.The fundamental question remains as to what relevance probabilities that are declared conditional on the outcome of certain events should hold for the actual posterior probabilities that you assign when you do learn of the outcomes.By the time that you observe the data,you may have come across further unanticipated but relevant information(or you may not, and this also is relevant information),and you may well have further general insights about the problem,by study of relevant literature,deeper mathematical treatment or careful data analysis.None of this corresponds to Bayesian conditioning.Indeed,I cannot remember ever seeing a non-trivial Bayesian analysis which actually proceeded according to the usual Bayes formalism.All of this illustrates the simple observation that there is no stronger reason why there should be a rule for going from prior to posterior beliefs than that there should be such a rule for constructing prior beliefs in the?rst place.(For example,any attempt to view conditional beliefs as the beliefs that you“should”hold were you to observe the conditioning event and“nothing else”is

Michael Goldstein415 doomed to self-contradiction,as the fact that you observed nothing else was not part of the original conditioning event,and would be informative were it to be included in the conditioning.)

For the above reasons,all attempts to present the Bayesian approach as a normative theory,which describes how we should,in principle,modify our beliefs given evidence, must be fundamentally incomplete.They are analogous to a similar discussion as to whether and when,say,a global climate model is right or wrong.This is the wrong question.We know that the global climate model di?ers from the actual climate-they are two quite di?erent things.Instead,our two tasks are?rst to identify why we consider that a particular climate model is informative for climate,and second to quantify the value of this information,by considering the residual uncertainty in climate behaviour, given the analysis of the climate model.

If we view the Bayes formalism as providing a model for belief change which is neither normative nor descriptive,then the natural questions are?rst,why do we consider this model relevant to actual problems of belief change,and second,how do we describe the discrepancy between this model and the reality of changing beliefs?Our answers to these questions are as follows;for details see Goldstein(1997).We begin by distinguishing between your current conditional probabilities P(A|B),P(A|B c)and your posterior probability P t(A)that you will assign at future time t after you have observed either B or B c.We need a temporal principle to link beliefs that you specify now with those that you will specify at time t.This link is provided by the temporal sure preference (TSP)principle,which is as follows.

“If you are sure that at future time t you will prefer the(small)random penalty A to the(small)random penalty B,then you should not now prefer B to A.”

TSP places a very weak requirement on your temporal preferences,which would certainly be satis?ed within the conventional Bayesian formulation.However,unlike the Bayes formalism,which seeks to make today’s speci?cation logically compelling for tomorrow’s revision of belief,TSP places our preferences in the right order,requiring logical certainty in the future to be compelling for our current belief evaluations.

If we accept TSP for the current inference,then we can show(see Goldstein(1997)) that this establishes the following stochastic relationship between conditional and pos-terior probabilities,namely that at the present moment you must make the speci?cation

P t(A)=P(A|B)+R,(1) where P(A|B)is the conditioning of A on the partition B=(B,B c),namely

P(A|B)=P(A|B)B+P(A|B c)B c,

where B,B c are the indicator functions for the corresponding events,and R is a further random quantity with

E(R)=E(R|B)=E(R|B c)=0.

416Subjective Bayesian Analysis This corresponds closely to the interpretation that we have earlier suggested for mathematical models of physical systems.For example,a climate model does not tell us what will actually happen,but instead is useful,for example,in giving us a mean forecast,with associated variance,whose value lies in reducing,but not eliminating,our uncertainty about climate behaviour.Similarly,from(1),we are justi?ed in viewing P(A|B)as providing a mean forecast for our future judgements,while the residual quantity R expresses the uncertainty in the conditional mean https://www.doczj.com/doc/3d2061769.html,rmally,the larger the variance of P(A|B)as compared to the variance of R,the more informative a formal Bayes inference based on conditioning on B will be for the actual posterior judgement on A.As the variance of P t(A)is?xed,we may both increase the variance of P(A|B)and decrease the variance of R by re?ning the partition B.

4.2Bayes linear inference

The above argument clari?es the logical status of a Bayesian analysis.It also frees us from the tyranny of conditioning.Even though(1)concerns probabilities,this relation can only be derived within a formalism which starts with expectation as primitive.In that formulation,probabilities are just expectation statements,and(1)is a special case of the following general result.Suppose that D is a vector of quantities which will be observed by time t.Given TSP,your current beliefs about your posterior expectation E t(X)for any other random vector X,speci?ed at time t,must satisfy the following relations:

X=E t(X)+S(2)

E t(X)=E D(X)+R,(3) where E D(X)is the Bayes linear mean for X determined by

E D(X)=E(X)+Cov(X,D)(Var(D))?1(D?E(D)

and R,S are further random quantities,with

E(R)=E(S)=Cov(R,D)=Cov(S,D)=Cov(R,S)=Cov(S,E t(X))=0.

The Bayes linear analysis is based on direct speci?cation of means,variances and covariances;for an overview of the Bayes linear approach to statistics see Goldstein (1999).From(2),(3),the Bayes linear analysis for X bears the same relation to the actual posterior judgement for X that the posterior judgement for X bears to the quantity X itself.Bayesian conditioning is simply the special case of(3)in which D comprises the indicator functions for a partition.

If conditioning is not the operation underpinning the Bayesian analysis,then the requirement of full probabilistic speci?cation can be seen as an arbitrary imposition. We may make full probabilistic speci?cations where this is natural and straightforward; this representation will maximise the proportion of uncertainty expressed by E D(X).

Michael Goldstein417 However,this is a re?nement of degree,not of kind.If the extra information is worth the e?ort in prior speci?cation and analysis that is required,then the full Bayes approach is worthwhile.Otherwise,a simpler analysis is appropriate.Placing the subjectivist anal-ysis within a logical framework which distinguishes between the model for the inference and the actual inference gives us control of the level of detail of our prior speci?cation and analysis,while reminding us of the requirement to relate the formal analysis to the larger inferential problem,which should always be our primary concern.Of course,this raises further questions as to precisely how such inferences should be conducted.This is largely unexplored territory;for theoretical underpinnings embedding statistical models derived from exchangeability judgements within this more fully subjectivist view,see Goldstein(1994).Subjectivist theory o?ers a language and framework rather than a complete description of belief representation and inference.Whether such a complete description could ever be provided is,in my subjective opinion,extremely doubtful.

5Pragmatic subjectivism

The practical objection to routine use of subjective Bayesian analysis is that it is too hard,because of the di?culty of specifying well founded prior distributions for the quantities of interest in complicated problems.Any analysis is a pragmatic compromise between what we might ideally wish to do and what it is feasible to do.In particular, we may make pragmatic compromises in the following situations.

5.1When it doesn’t a?ect the answer

As we have suggested,it is often instructive to consider how wide a range of prior judgements may be brought into broad agreement by the experimental data.If we have su?cient data to overwhelm most reasonable assignments of prior beliefs,then we can sidestep the subjectivist speci?cation,unless we are interested in analysing the way that our beliefs have changed as a result of the experiment.

We may take a similar shortcut if the amount of data is rather less,but we feel that our beliefs are su?ciently vague that they may be overridden by even a small amount of data.Alternatively,there may be some aspects of our speci?cation which,while present for formal completeness in our uncertainty description,have very little impact on the main issues which the analysis addresses.

In all such cases,we may employ simple automatic methods for prior speci?cation, as we may demonstrate by analysis that this will have only a small e?ect on the?nal conclusions.

5.2When we are making a preliminary study

We make careful subjectivist speci?cations because we want to infer meaningful uncer-tainties about real situations.However,much of scienti?c investigation is preliminary

418Subjective Bayesian Analysis to this stage.We may study data arising in unfamiliar contexts in order to suggest qualitative ideas which we may subsequently take forward in more careful and critical quantitative studies.If a simple analysis can bring out the most important messages of the data quickly and cleanly,then this may be su?cient.Similarly,we may study simple models for complicated phenomena to try to gain qualitative insights into the kind of behaviour that we might anticipate the system to follow,and therefore to guide us in producing more realistic system models.In such cases,a full subjective prior speci?ca-tion may be irrelevant and unnecessary to the aims of the study.If we want to produce Bayesian output,then a simple vague or objective prior speci?cation may be adequate to the analysis.All that we must be sure of in such cases is that everyone understands that the analysis does not claim to o?er well-sourced uncertainty statements about real problems.

5.3When the problem is not important

We have observed that the practical quantity governing the value of the subjective analysis is the“pain to gain”ratio.For a given level of stochastic complexity,the subjective analysis takes the same amount of e?ort when nobody cares about the answer or when the answer is of enormous https://www.doczj.com/doc/3d2061769.html,mon sense tells us not to waste too much e?ort in producing impeccable solutions to problems which were not worth careful consideration in the?rst place.The only quali?er to this is that many people progress through their careers by tackling problems with increasingly important consequences.It is arguably a good idea to obtain practice in careful analysis on problems where mistakes will not have disastrous consequences,rather than waiting for a critical problem on which to gain subjectivist Bayesian skills.

5.4When we are under-resourced

Suppose that a study aims to make uncertainty statements which will be applied to an important real world problem and for which our prior speci?cation will be highly relevant to the conclusions,but that we are not resourced adequately to support a full subjectivist analysis.We are now faced with di?cult choices,for which there is no fully satisfactory solution.We may decide that the value of the incomplete analysis outweighs possible misinterpretation of the results.There are no strict guidelines as to how to conduct an analysis under such resource constraints.In principle,we should identify those aspects of the problem which are most sensitive to prior judgements and those aspects for which prior knowledge appears most likely to be well formed and speci?c,and aim to give careful subjective representation to each such aspect.For the remaining features of the analysis,we may be able to employ simple standard prior forms,without too much damage to our conclusions,and ideally,we would be able to demonstrate this by supporting sensitivity analysis.Failing this happy outcome,we should proceed with extreme caution,making clear to all participants the degree of approximation in our conclusions,and,if necessary,identifying precisely the additional resource which would be required in order to produce an analysis whose conclusions

Michael Goldstein419 could be used with con?dence.

5.5On not being be too pragmatic

While pragmatism is an excellent quality,it should not be used as an excuse for not doing the job properly.My impression is that,in many?elds,people may indeed be taking an overly pragmatic stance.Thus,experimenters will argue for large budgets for their experiments,and modellers will invest enormous e?orts in constructing and running their models,and this process is well understood and accepted within science. However,very few people outside of the Bayesian community are even aware of the potential bene?ts of carrying out a careful Bayesian statistical analysis and so it is up to us to be aggressive in making the case for the value of such an approach.When enormous time,e?ort and investment has gone into experimenting,collecting data, theorising and modelling,it is unfortunate,to say the least,if nobody takes the?nal step of bringing all these considerations together into a coherent statement as to what our uncertainty should be as a result of all of this e?ort.

6Concluding comments

The subjective Bayes approach is alive and well and proving very successful in many important practical applications.However,much of the potential of the approach is still to be realised.Subjectivist analysis may appear daunting,but what is di?cult is making reasoned judgements about complex situations within any framework at all. The subjectivist approach does not make these di?culties vanish,but it does o?er a coherent language and tool set for analysing all of the uncertainties in complicated problems,and therefore provides the best method that I know for analysing uncertainty in important real world problems.But who,in practice,will carry out such analyses?

Modellers are skilled at modelling,theorists develop theory,experimenters spend their time experimenting and statisticians tend to view their role as analysing data. These are all essential skills.But we are missing the specialism which moves beyond these comfort zones and puts all these activities together.When we properly recognise, develop and apply the ideas and methods of subjectivist analysis,then we will?nally be able to carry out that synthesis of models,theory,experiments and data analysis which is necessary to make real inferences about the real world.

References

Bernardo,J.and Smith,A.(1994).Bayesian Theory.Chichester:John Wiley. Craig,P.S.,Goldstein,M.,Rougier,J.C.,and Seheult,A.H.(2001).“Bayesian Forecasting Using Large Computer Models.”JASA,96:717–729.

Craig,P.S.,Goldstein,M.,Seheult,A.H.,and Smith,J.A.(1996).“Bayes linear

420Subjective Bayesian Analysis strategies for history matching of hydrocarbon reservoirs.”In Bernardo,J.-M.et al. (eds.),Bayesian Statistics5,69–98.Oxford:University Press.

de Finetti,B.(1974).Theory of probability,vol.1.New York:Wiley.

Garthwaite,P.,Kadane,J.,and O’Hagan,A.(2005).“Statistical methods for eliciting probability distributions.”J.Amer.Statist.Assoc.,100:680–701.

Goldstein,M.(1994).“Revising exchangeable beliefs:subjectivist foundations for the inductive argument.”In Freeman,P.and Smith,A.F.M.(eds.),Aspects of Uncer-tainty:A Tribute to D.V.Lindley.Wiley.

—(1997).“Prior inferences for posterior judgements.”In Chiara,M.L.D.,Doets,K., Mundici,D.,and van Benthem,J.(eds.),Structures and Norms in Science.Volume Two of the Tenth International Congress of Logic,Methodology and Philosophy of Science,Florence,August1995,55–71.Dordrecht:Kluwer.

—(1999).“Bayes linear analysis.”In Kotz,S.et al.(eds.),Encyclopaedia of Statistical Sciences,update volume3,29–34.Wiley.

Goldstein,M.and Rougier,J.C.(2005).“Probabilistic formulations for transferring inferences from mathematical models to physical systems.”SIAM journal on scienti?c computing,26:467–487.

Howson,C.and Urbach,P.(1989).Scienti?c reasoning:the Bayesian https://www.doczj.com/doc/3d2061769.html, Salle,Illinois:Open Court.

Kennedy,M.and O’Hagan,A.(2001).“Bayesian calibration of computer models(with discussion).”Journal of the Royal Statistical Society,Series B,63:425–464.

Lad,F.(1996).Operational subjective statistical methods:a mathematical,philosoph-ical and historical introduction.New York:John Wiley.

Lindley,D.(2000).“The philosophy of statistics(with discussion).”The Statistician, 49:293–337.

O’Hagan,A.and Forster,J.(2004).Kendall’s Advanced Theory of Statistics Volume 2B:Bayesian Inference Second Edition.Oxford University Press.

Robert,C.(2001).The Bayesian choice(2nd edition).New York:Springer-Verlag. Santner,T.J.,Williams,B.J.,and Notz,W.I.(2003).The Design and Analysis of Computer Experiments.Springer.

Woo?,D.,Goldstein,M.,and Coolen,F.(2002).“Bayesian Graphical Models for Software Testing.”IEEE Transactions on Software Engineering,28:510–525.

(完整word版)《Excel图表制作》教案

Excel图表制作 一、教学目标: 知识技能:1、理解EXCEL图表的作用 2、熟练掌握利用图表向导建立图表的操作 3、理解并掌握图表(柱形图、折线图和饼图)类型的选择 4、理解并掌握图表源数据的选择 情感态度价值观:养成善于发现问题、积极思考、并乐于与同伴交流等良好品质 二、教学重、难点 教学重点:1、利用图表向导建立图表的操作 2、图表类型的选择(柱形图、折线图和饼图) 3、图表源数据的选择 教学难点:图表类型的选择与图表源数据的选择 三、教学方法 教师引导、任务驱动下的学生自主、探究、交流学习 四、课时及教学环境 两课时,多媒体教学网络 五、教学过程 1、引入 直接引入。强调图表制作是Excel的一项重要内容,引起学生重视。 2、图表的作用 本环节使用一个学生的成绩表为例,向学生表明,繁多的数据在一起,由于凌乱,很不容易了解数据背后含有的意义。而将数据转成图表之后,其含有的意义就很容易表现出来。从而得到结论:图来源于表格数据,却能更直观、清晰地反映数据的含义。趁此机会向学生展示几种图表的类型,略加说明各类型的表现优势,同时引发学生学习的兴趣。 3、图表元素讲解 看了这么多图表,也了解了图表的作用,大家是不是也跃跃欲试,想动手制作一个漂亮的图表呀?哎,别急,在具体学习如何创建图表前,我们先来用一个例子来好好认识一下这个图表。 此环节主要通过一个三维簇状柱形图来让学生了解构成图表的元素。提醒学生可以通过Excel提供的辅助帮助功能来认识图表元素。在这个过程中要注意以下几点: 1)说明在图表中一般必须要有的元素,如图表标题、数值轴、分类轴及标题、图例等。也要提醒学生,可以根据题意添加或删除某些元素。这里以“月份”为例,由于通过分类轴可以明确分类轴的含义,所以可以“清除”月份标题。通过这个讲解,为下一个注意点服务,即右键操作。

图书馆空间设计研究

图书馆空间设计研究 发表时间:2018-01-02T14:52:34.340Z 来源:《建筑知识》2017年24期作者:陈华新赵文华张梦雅秦佑红刘菲[导读] 本方案选择将对于木材质的理解与荷兰风格派的设计理念相结合,旨在创造出适合人的行为尺度的建筑。 (山东建筑大学山东济南 250101)【摘要】图书馆,顾名思义,是一个搜集、整理、收藏图书资料以供人阅览、参考的机构。本方案选择将对于木材质的理解与荷兰风格派的设计理念相结合,旨在创造出适合人的行为尺度的建筑。从建筑的形式、要素、实用性、功能性等各方面出发做了充分考量,以建造这样一个图书馆空间:一个一层平面的建筑,打破墙体,消除内外空间的隔阂,大量通透的玻璃将光线直接引入室内,形成一种积极的暗示(一种适宜人阅读的环境)。图书馆的各个功能空间被划分成矩形平面,且建筑不可刻意追求象征意义,不刻意追求视觉需要及建筑的高度,而是一种俯伏于地面上的建筑,建筑的本意便是让人容身,使人使用、居住的舒服。【关键词】木质;荷兰风格派;人的尺度;俯伏【中图分类号】TU-024 【文献标识码】A 【文章编号】1002-8544(2017)24-0009-02 1.课题研究背景及现状分析 社会历史是不断向前发展的,而知识的传承毫无疑问在人类的发展历程中占据了核心地位。大学生是社会发展的最重要的储备力量,如何促进大学生的知识储备、形成正确的人生观价值观则首当其冲。中国教育新闻网曾发布过一组调查新闻显示:大学生大部分时间花在了社交网络上,而日常阅读则是少之又少。图书馆以其悠久的历史不断传承、发展,其建筑形制也历经历史、战火灾难的洗礼几经沧桑延续至今。图书馆有不同的类型划分,我国则按照管理体制、馆藏文献范围、用户群、图书载体等的不同划分为不同的标准类型。通过现有的图书设计案例的分析发现中国高校图书馆目前主要存在着以下几个问题:(1)图书馆的建筑特点缺乏个性的表现,我国图书馆历史由来已久,但现代意义上的图书馆一词于19世纪末从日本传入我国。我国现代图书馆的发展起步也较晚,对图书馆的设计重视不足,通常只是满足了图书馆设计所必须的要求而对光线、空间、人的尺度感受往往忽视。 (2)图书馆的功能定位在一定程度上决定了读书群体的阅读方向性,高校中为了考试而读书的现象不容忽视。 (3)图书馆模块式建筑设计,导致了在荷载上取值、造价比较高,进深、室内空间比较大,容易显得单调和枯燥。由于采取全人工采光,所以导致通风照明等能源消耗过大,且建筑设计造型显得比较单调,缺乏个性化设计思路。 (4)缺乏人性化设计,大部分校图书馆信息落后跟不上社会发展脚步,馆藏图书陈旧,就直接导致了满足不了学生对读书的需求。 校图书馆蕴含了巨大的文化价值:平等性的文化诉求得到了充分的抒发,不论老师、学生、校内各种教职员工人人都有获得入馆学习交流的权利;引导学生在形成健全人格、人生观、价值观之外,缩小信息不均的差距,促进学生在高校生活中可进一步有机会发展自己的潜在能力;可作为高校的文化符号或文化形象而存在,所以我们不能将其忽视。 2.基地选址 图书馆的位置选址在山东建筑大学,在山东建筑大学东北门入口处路北,与火车餐厅相连,南面为教学楼,往西则是学生餐厅、学生宿舍,北面则是偌大的体育场。 (1)东西向的博学路是校内人流量最大的一条路之一,它将学生宿舍、餐厅、教学楼连接在一起,而将图书馆位置选址在此则充分考虑了宿舍-餐厅-图书馆三点一线,可避免学生行路的时间,提高学习生活的效率。 (2)建筑的南面为向阳部分,光照充足,适合人学习、阅读,这将对功能区域的划分起到决定性作用。对建筑的流线、光影、人口密度的考量对建筑基地的选址以及整体方案设计起着至关重要的作用。光与影是展示建筑空间、表现造型艺术、美化建筑环境的重要手段。通过研究光影与建筑造型的关系,巧妙的运用光影可以获得意境不凡的建筑艺术效果。勒?柯布西耶曾说建筑艺术的要素是:墙与空间,光与影。由此可见光影对于空间的重要性。 本方案从建筑的功能、体量、表皮、时间、空间、光、色彩、质感等方面都做了深入考量,并依据荷兰风格派蒙德里安的黄金分割原理对空间进行划分。建筑是作为环境的一部分而存在的,故将灰空间作为联系室内室外的一个过渡空间把室内室外有机联系在一起。在建筑体上或大或小的玻璃窗,这些玻璃窗保证了光线的引入,在形式上也连贯起来。 3.图书馆建筑空间分析 本方案设计灵感源于荷兰风格派,坚持正交体系在建筑中的运用。利用垂直和水平线等作为设计元素的表现手法,破除个体隔阂,使室内室外相互融合成为统一连续的整体。建筑要坚持功能合理重视与现有环境的关系,美感在于合情合理耐得住长久的体验,而非一时激动的反响,所以建筑要表现的适宜、优雅,不能过于突兀。 3.1 建筑材质分析 (1)在本次设计中大量运用了玻璃以及木材,因为图书馆是个阅读空间,对光线有着极高的要求,而大量运用人工照明的话成本会增加,故在建筑体上设置了大量玻璃门窗。玻璃门窗用于建筑的历史非常久远,甚至难以考据,而1951年的纽约利华大厦对于玻璃的运用,使得玻璃此后迅速成为高层建筑首选的墙体材料。玻璃作为建筑材料同时具备四大特点:透明、高强、轻质、耐久,而其他墙体材料则很少兼具这些特点。 (2)本次设计大量运用木质材料,很大程度上是由于木制的材料在使用时更能唤起人的亲切感,更能达到建筑设计的初衷——一个平易近人,平等性的呼吁。其次,图书馆空间会大量的运用书格,木制材料在兼备功能的同时达到了形式美感的要求。 建筑外观也直接运用了木格这一形式起到了装饰作用,并且室内室外达到高度的和谐和统一。虽然只是水平垂直的线条组成的建筑外观形式,但有序的、大量的重复排列形成了一种形式感、秩序感。建筑的平面轮廓或凸出或凹进打破了四方盒子的呆板形象,大门入口处的长长走廊以及次入口出的可作为展厅的灰空间都将室内室外有机联系起来,为学生阅读、交流提供了一个绝佳的阳光饱满的静谧空间。 建筑占地面积为1200㎡左右,室内高度为8m,除了必要的学习区、展厅、阶梯教室、咖啡区、服务总台、卫生间,其二层是一条长长的2m宽的过道,可俯瞰整个建筑空间的内部(主要起游览、参观作用)。室内十字形的交通路线,合理准确的将各个空间有机联系在一起,建筑的功能性发挥到最大化。

国内外研究现状总结

1、研究意义: 随着我国国民经济和城市化建设的飞速发展,大型商业综合体在当今商业创新模式的潮流和城市空间有机化、复合化的趋势下应运而生,数量日益增多,体量越来越大。这类公众聚集场所一般具有功能繁多、空间种类丰富、人流量大、火荷载大等特点,一旦发生火灾,容易导致重、特大人员伤亡和直接经济损失。近年来大型商业建筑火灾造成的人员伤亡事件屡有发生。国外的发展经验表明,当一个国家的人均GDP达到1000-3000美元时,社会将会处于一个灾难事故多发阶段,这表明我国当前及今后较长的一个时期,火灾安全形势依然十分严峻。 飞速发展的大型商业建筑,使用功能日趋复杂、集约,这给大型商业综合体的安全疏散设计带来了十分严峻的挑战。安全疏散,就是在发生火灾时,在允许的疏散时间范围中,使遭受火灾危害的人或贵重物资在楼内火灾未危及其安全之前,借助于各种疏散设施,有组织、安全、准确、迅速地撤离到安全区域。 大型综合性商业建筑的使用功能高度集中,现行规范都无法对其建筑形态和业态分布做出明确的规定,基于以往经验及科研成果制订出来的建筑防火设计规范难以适应新的需要,实践中经常遇到大量现行规范适应范围无法涵盖或规范条文无法适应建筑物设计形式的尴尬局面。现代大型商业综合体建筑的设计往往突破了现行规范,因此在一些经济发达的地区,也将性能化的防火设计理念引入到了设计之中,它已成为未来防火设计发展的趋势。 商业街建筑由于其独特性,有关消防设计也有别于一般的商业建筑。比如,商业街是否作为一个整体建筑考虑其消安全疏散设计,是否应限制商业街建筑的层数,长度和宽度,步行街是否考虑作为人员疏散安全区域及其条件等等这些问题都有待于进一步的调研及深入分析。 同时,由于这类建筑火灾危险性特别大,人员密度大,疏散困难等原因,研究大型商业建筑火灾下人员疏散的安全性,以最大限度的防止火灾发生和减少火灾造成的损失,就具有十分重要的意义。由于我国火灾基础研究的滞后在制定国家消防技术规范时存在一些弊端和不合理之处。这些弊端给复杂的商业建筑空间设计带来很多的局限性,因此要使大型商业建筑有效的快速发展这就需要我们找到新的途径和新的思路来保障建筑的安全疏散。 大型商业综合体的人员安全疏散设计应该综合相关多方因素全面考虑。处方式建筑防火安全疏散设计理念适应不了现代建筑的发展趋势,我们需要借鉴心理学等理论,研究发生火灾后,大型商场内人员在这样的环境中的空间认知能力和行为模式;从空间组织设计的角度出发,结合建筑性能化防火设计的理论全面的进行防火安全疏散设计的研究。这有助于科学合理的进行大型商场的建筑防火设计,当灾害来临时为人们提供一个可靠的安全疏散系统,同时又利于人们充分的使用空间的目标;同时,该课题的研究为促进大型商场发展作出努力,使得大型建筑在城市发展的新形式下可持续的发展。 大型商业综合体中防火分区面积往往超出了规范中对防火分区面积的限制,疏散出口的数量以及布置方式等问题随之产生,这些问题都有待进一步深入研究。本文从大型商业综合体的自身特性入手,运用建筑学、消防安全学和行为心理学等领域的相关知识,对火灾下大型商业综合体内人员疏散的安全性能进行了研究和分析,总结出大型商业综合体人员安全疏散的难点和重点问题,最后针对这些问题提出了一些优化策略和方法,并分析了应用部分方法的实际工程案例。为大型商业综合体的人员安全疏散设计提供参考。 2、国内外研究现状: (1)国外研究现状 国外发达国家对于大型商业综合体的设计,除了能依据本国的规范进行设计的之外,超出规范规定内容的往往利用了性能化的防火设计。欧美发达国家在这项研究中处于领先的地位,已开发出了很多计算及模拟软件。如FDS、SIMULEX和STEPS等等。 上世纪八十年代,己有一些国家颁布了专门的性能化防火设计规范。所以发展至今,已形成了相对完善的体系。国外的设计者在做一些大型的商业建筑时,都会采用性能化的防火设计。1971年,美国的通用事务管理局形成了《建筑火灾安全判据》。20世纪80年代,在美国实施了一个国家级的火灾风险评估项目,其结果形成了FRAMWORKS模型。1988年美国防火

EXCEL高阶图表制作教程

EXCEL高阶图表制作教程 今天和大家分享一个炫酷的图表技巧,先看效果: 只要光标滑过不同的商品名称,图表就会自动变化,是不是很炫酷啊。步骤一:准备数据源

步骤二:输入代码 Alt+F11 打开VBE窗口,【插入】→【模块】复制如下代码到模块中,退出VBE窗口:Function techart(rng As Range) Sheet1.[g1] = rng.Value End Function

步骤三:输入公式 在G1单元格中输入任意一个商品名称,如牛仔裤。 G2输入公式,下拉至G13: 选中G1:G13,【插入】→【折线图】 步骤四:美化图表 设置折线图为无线条。 设置数据点样式,添加垂直线。 此处省略具体步骤,大家可以根据需要和喜好,设置出不同样式不同风格的图表样式。 最终效果如下:

步骤五:输入公式在图表上方,输入模拟图表标题的公式。 =G1&"2015年销售趋势" 在图表下方,输入模拟坐标轴的公式。 本例以J15:K16单元格区域为例,依次输入以下公式: =IFERROR(HYPERLINK(techart(B1)),"◆"&B1&"◆"&REPT(" ",99)) =IFERROR(HYPERLINK(techart(C1)),"◆"&C1&"◆"&REPT(" ",99)) =IFERROR(HYPERLINK(techart(D1)),"◆"&D1&"◆"&REPT(" ",99)) =IFERROR(HYPERLINK(techart(E1)),"◆"&E1&"◆"&REPT(" ",99))输入公式后的效果如下:

好看的图表制作软件

好看的图表制作软件 导语: 好看的图表制作软件,专业的图表能够帮助你简化沟通,漂亮的图表却能让人眼前一亮,瞬间吸引众人的目光。这就是为什么有人即便已经学会了如何用Excel制作图表,却依然走在美化图表的路上的原因。 免费获取商务图表软件:https://www.doczj.com/doc/3d2061769.html,/businessform/ 当数据变得易于阅读和理解时,我们就容易记住它,并在以后使用到这些数据,充分发挥数据的影响力。而且,建立起各数据之间关系之后,可以从中发现仅阅读原始数据无法发现的一些信息,更有利于在管理决策过程中使用。 什么软件可以制作出好看的图表? 图表制作通常我们会使用Excel、Word制作,但一些稍复杂的图表使用这些软件制作便有些吃力,或者说又要借助其他软件才能绘制。亿图图示,一款专业绘制各类图表的软件。软件可以绘制柱状图、饼图、条形图、雷达图、气泡图等

一系列图表,操作使用简单,无需特意学习便可绘制,图种齐全,是绘制图表的不二选择。 使用亿图图示专家绘制图表有何优势? 1.可以通过模板、例子,快速创建出专业的图表; 2.图表数据可以进行实时修改; 3.自带多种主题、样式,可以一键更改整个主题风格; 4.可以通过导入数据快速创建图表; 5.可以将图表中的数据直接导出为Excel; 6.支持跨平台操作,可用于windows、Mmac以及 linux系统;

7.可以一键导入导出为visio,以及导出PDF、SVG、office、PowerPoint、 图片格式的文件; 8.支持云储存,个人云和团队云; 9.所见即所得的打印方式。 丰富的模板和例子:

未来住宅公共空间设计趋势及对策研究

未来住宅公共空间设计趋势及对策研究 发表时间:2019-05-08T09:58:46.520Z 来源:《建筑学研究前沿》2019年1期作者:罗丹芝曾海玲 [导读] 当前,我国住宅市场面临着众多的矛盾,如房价走高与住宅利用率较低的矛盾,资源环境制约与居住便利性要求提高的矛盾,为了解决这些矛盾,就需要对未来住宅公共空间设计的趋势进行研究。本文先围绕未来住宅公共空间设计趋势进行简要的分析,并对其应对策略进行探讨。 罗丹芝曾海玲 筑博设计股份有限公司佛山分公司广东佛山 528253 摘要:当前,我国住宅市场面临着众多的矛盾,如房价走高与住宅利用率较低的矛盾,资源环境制约与居住便利性要求提高的矛盾,为了解决这些矛盾,就需要对未来住宅公共空间设计的趋势进行研究。本文先围绕未来住宅公共空间设计趋势进行简要的分析,并对其应对策略进行探讨。 关键词:住宅设计;公共空间;设计趋势;应对对策 前言 在1978年,中国理论界提出了住房商品化,然后再到1981年,广州以及深圳试点商品房开发,历经几十年的发展,我国人民的住房需求有了较大的改善。与此同时,也产生了许多新的问题,如房价持续走高与住宅利用率降低的矛盾,人口聚集与住宅稀缺程度提高的矛盾。基于此,围绕未来住宅公共空间设计趋势以及对策进行研究具有重要的意义。 1、住宅市场现状分析 在对住宅空间设计趋势进行研究之前,我们需要对我国目前的住宅市场所存在的矛盾进行深入的剖析: (1)在我国住宅市场中,存在着人口持续聚集和资源稀缺程度提高的趋势。在我国一些大城市中,拥有更多的教育、医疗等方面的资源,而且拥有更多的就业机会,所以每年都可以吸引许多的就业人员。另外,土地资源变得越来越稀缺了,许多大城市的土地已经演变为寸土寸金的局面,国家政府部门出台了相应的土地供给政策,这导致土地之上的房屋供给增长难以跟上市场发展的需求,两者之间存在着较深的矛盾【1】。 (2)现如今,各城市的房价逐渐攀升,而住宅利用率却非常的低,这两者之间的矛盾也愈演愈烈。当前,随着房价的持续上涨,人们的购房压力逐渐加大,而且对于许多的消费者而言,购房仅仅是为了拥有一个住所,有很多人没有用到厨房,甚至没有用到客厅。但是,在购房之时,这些功能区域基本上是一体的。 (3)当前,在我国住宅市场中,存在着情感交流需要与公共空间稀缺的矛盾。随着人民生活水平的提升,人们的温饱问题得到了解决,这使得他们对自己的精神需求有了更高的要求。大家渴望与邻居之间进行交往,渴望得到朋友。然而,在商品住房的设计中,很好考虑公共空间,除了过道、电梯等区域,邻里之间的公共交流空间非常少。 在这些矛盾的聚集下,住宅市场会发生许多的变化,如公共空间由以往的可有可无变成人人都需要的必需品。单一住宅的平均面积会有所下降,而资源的利用率就会得到提升,并且住宅的功能区会发生变化,变得越来越细。因此,在未来的发展中,住宅公共空间必然会逐渐的扩大,这必将成为一种趋势【2】。 2、住宅公共空间的设计趋势分析 在住宅公共空间的设计过程中,需要对目标家庭细分进行考虑,并对其住宅市场的特殊性进行考虑。由于住宅具有天然的私密性,比办公商用等要求更高,所以对细分市场的选择尤为重要。在未来住宅的设计规划中,公共空间的扩大化趋势也会从某些细分市场领域开始启动。住宅的需求与许多方面有关,例如与家庭的结构密切相关。现如今,中国家庭结构呈现出以下几大特点:(1)规模小型化、(2)类型多元化、(3)结构核心化。因此,在住宅公共空间设计过程中,需要对家庭的结构进行深入的研究。一人户、二人户的家庭比例持续提高,与此同时,一对夫妻的核心家庭比例逐渐提升。另外,“空巢家庭”、“单亲家庭”等类型也有了较大的提升,“四二一”式家庭结构也非常的常见。在住宅公共空间的设计上,应当对住宅整体的家庭定位进行考虑,因为家庭的结构如果不同,那么他们的住宅需求也会有所差异。 其次,对于不同的家庭结构类型,在住宅设计中,可以设置多样化的户型以供购房者选择。例如,如果是一对夫妻的核心家庭,而夫妻两人具有职业,那么他们对厨房、客厅以及餐厅的需求会显著降低,并且还有较大的可能接受公共食堂的这种形式。在对目标销售人群进行定位时,如果是目标对象是一个以年轻白领夫妻家庭为主,那么就住宅设计中可以减少厨房、客厅等公共空间面积。与此同时,可以设置商务会谈空间,或者是设置休闲娱乐空间。如果目标销售人群是“四二一”式家庭,那么他们就迫切需要一个更大的公共聚集空间,所以在设计过程中,可以采取星型分布的方式,在三个住户的中间构建一个公共客厅。这样一来,不仅能够满足每户家庭的私密性,而且也能够增加双方交流互动的空间【3】。 3、公共空间扩大趋势下的配套政策建议 住宅公共空间的扩大化虽然可以解决住宅市场中的一些矛盾,但是也会产生许多的新问题。如,公共空间的管理维护问题,公共空间的产权及交易问题等,都会阻碍公共空间设计的效果。而要解决这些问题,就需要有配套政策的支持。 首先,可以先从某些领域着手,推动公共空间扩大的尝试。如,针对城市廉租房的建设,可以对资源的集约化进行考虑,对公共空间的设计予以更多的考虑。在满足住户需求的背景下,尽可能的降低建设成本。此外,也可以鼓励开发一些针对特定目标人群的项目。例如,鼓励养老机构或者其他机构在老年人公寓的建设上进行研究,使住宅公共空间的设计能够满足老年人群体的特点以及需求。又比如,可以鼓励开发青年公寓,在私密空间的设计中,以卧室和卫浴为主,然后再配上不同功能定位的公共空间,如健身房或者会客室等等。通过对试点进行有针对性的探索,逐渐积累住宅开发以及设计的经验,为后期的规划设计打下坚实的基础。 其次,国家需要发挥自己的职能,在立法层面上着手,对住宅公共空间的建设以及产权等问题进行明确。由于住宅公共空间具有公共性的特点,如果管理不到位,或者职责不明确,那么就会产生管理缺失的问题,会成为无人负责的三不管地带。另外,在管理到位的情况

(讲稿)毕业设计论文指之国内外设计研究现状的写法和范文

[讲稿]毕业论文指之国内外研究现状的写法与范文 毕业论文指之“国内外研究现状”的撰写 一、写国内外研究现状的意义 通过写国内外研究现状,考察学生对自己课题目前研究范围和深度的理 解与把握,间接考察学生是否阅读了一定的参考文献。这不仅是毕业论文 撰写不可缺少的组成部分,而而且是为了让学生了解相关领域理论研究前沿,从而开拓思路,在他人成果的基础上展开更加深入的研究,避免不必 要的重复劳动或避免研究重复。 二、国内外研究现状写法 在撰写之前,要先把从网络上和图书馆收集和阅读过的与所写毕业论文 选题有关的专著和论文中的主要观点归类整理,找出课题的研究开始、发 展和现在研究的主要方向,并从中选择最具有代表性的作者。 1. 在写毕业论文时,简写课题的研究开始、发展和现在研究的主要方向, 最重要的是对一些现行的研究主要观点进行概要阐述,并指明具有代表 性的作者和其发表观点的年份。 2. 再者简单撰写国内外研究现状评述研究的不足之处,可分技术不足和研 究不足。即还有哪方面没有涉及,是否有研究空白;或者研究不深入; 还有哪些理论或技术问题没有解决;或者在研究方法上还有什么缺陷等 等。 3. 最后简略介绍发展趋势。 三、写国内外研究现状应注意的问题 1.注意写的是把研究现状,而不是写课题物本身现状,重要体现研究。例如,写算法的可视化研究现状,应该写有哪些专著或论文、哪位作者、有什

么观点等;而不是大量算法的可视化研究何时产生、有哪些交易品种、 如何演变,此只需一笔带过,也是对研究的一种把握。 2.要写最新研究成果和历史意义重大的研究成功,主要写最新成果。 3(不要写得太少或写的太多。如果写的少,说明你查阅的材料少;如果太 多则说明你没有归纳,只是机械的罗列。一般2-3 页A4 纸即可。 4.如果没有与毕业论文选题直接相关的文献,就选择一些与毕业论文选题比较靠近的内容来写。多从网络上找资料,学习和练习。 “国内外研究现状”的撰写范文 在计算机图形学领域,三维可视化是一个重要的研究方向,许多研 究人员己经进行了大量卓有成效的研究,并有许多成熟的技术己经应用 到实际中,出现了大量的优秀的可视化软件产品,如3DMAX、MAYA、EVS、AVS 等。这些产品主要应用于游戏、电影动画、工业设计以及其它专业领域的研究,而与GIS 联系较少。 可视化理论与技术用于地图学与GIS 始于90 年代初。1993 年,国际 地图学协会(ICA)在德国科隆召开的第16 届学术讨论会上宣告成立可视化委员会(CommissionOnVisualization),其主要任务是定期交流可视化技术在地图学领域中的发展状况和研究热点,并加强与计算机领域的协作。1996 年该委员会与美国计算机协会图形学专业组(ACMSIGGAPH)进行了跨 学科的协作,制订了一项称为“CartoProiect"的行动计划,旨在探索计算机图形学领域的理论和技术如何有效地应用于空间数据可视化中,同时 也探讨怎样从地图学的观点和方法来促进计算机图形学的发展。1998 年 2 月由B(H(Mccormick 等根据美国国家科学基金会召开的“科学计算可 视化研讨会"的内容撰写的一份报告中正式提出了“科学计算可视化

空间发展规划文本

宜居xx专项规划之 城市空间发展规划 xx县规划局 xx市xx城市规划设计院 xx市xx县空间发展规划 一、总则 (一)为适应统筹城乡发展,加快我国城市化进程和重庆直辖市建设新形势的需要,积极、科学地指导和推进xx县城的建设和发展,根据xx县城总体规划(2003-2020)及相关规划,编制《xx县空间发展规划》。本次规划成果中文本和图纸是xx城市建设的法定指导性文件,二者不可分割并具法律效力。本规划一经批准,城市规划区范围内的各项土地与空间利用规划及一切建设活动,必须遵守本规划。 (二)规划依据: 1、《中华人民共和国城乡规划法》(2007.10公布) 2、《xx县国民经济和社会发展第十一个五年规划纲要》 3、《城市用地分类与规划建设用地标准》(GBJ137—90,1990.7发布) 4、《重庆市xx县城总体规划(2003—2020)》 5、《xx县县域城镇体系规划(2002—2020)》 6、《xx县xx镇土地利用总体规划》 7、《xx县四规叠合实施方案》 8、《xx县城乡总体规划(2007-2020)》 (三)规划区范围:xx县域1518.73平方公里。 xx县城市规划区范围包括xx镇、xx镇、xx镇行政辖区,共计xx KM2。近、中期不包括xx镇和xx镇,远期将xx镇和xx镇纳入。规划拟形成三个城市组团:主城区、xx组团、xx组团。本次规划的主要范围为主城区。主城区位于xx镇,西起xx、北含xx镇xx村、xx村;东含xx、xx村、xx村以及xx村;南包xx永兴村、xx村,总面积xx KM2。

二、xx城市发展现状 (一)xx城市建设现状特征 1、城区建设用地基本情况 2008年主城区城市建设用地面积为12平方公里,人均城市建设用地为100平方米。城市毛容积率为1.2。 2、住房建设保持快速增长态势。xx县未来住房供给量较大,住宅新开工建设量从2010年开始将逐年增高,保持增长态势。现状居住用地的规模占现状城市建设用地的比例偏高,呈现增长模式。 3、公共设施建设日益增强。2008年公共设施用地的拓展量达到1.5平方公里。同时,近年来城市公建建筑逐步开始建设,南阳公园正加速建设。其中,体育场、体育馆、滨河公园、天宝寨公园等大型公共服务设施的陆续通过选址或建设,将有利于完善城市的各项功能。 (二)城区空间发展的必要性 1、居住用地拓展呈现超前发展态势 2008年县城各类城市建设用地构成比例与总规的对比分析中,居住用地占城市建设用地的比例较大。 2、公共服务设施建设仍需同步加强 公共服务设施的建设滞后于居住建设,居住用地在不断增加的同时,公共设施用地却未跟上,与居住建设量的增长相匹配还存在一定差距,公共设施配套仍需同步加强。 3、城市绿地建设比例偏低,分布不均衡 绿地在总规中要求占总建设用地的比例为16.16%,而现状所占比例低于总规确定的用地比例,公园绿地建设速度滞后于城市建设速度,特别是大、中型城市公园建设投入不足或进度缓慢。另外,在新的绿地规划建设中,也呈现出区域不均衡发展。 4、工业用地产出强度需要提高 虽然近年来工业发展势头迅猛,但工业用地产出强度待提高,使用效率偏低,工业用地集约化程度和使用强度有待进一步提高。 5、xx景观、xx景观走廊建设要加强 目前,县城xx路的景观建设正在加强,党校—xx段,xx转盘—污水处理厂—xx 中学拟加紧建设,但县城体现公共功能的建筑和休闲空间相对较少,缺少通滨河的视觉景观廊道和开敞空间。xx山的山脊线在县城缺少视觉廊道。 三、宜居xx空间发展 (一) 宜居xx建设与空间发展 按照重庆市《宜居城市建设指标体系及评价标准》,直接同城市空间规划相关联的指标有:人均住房建筑面积、城市毛容积率、中心城区毛容积率、人均城市建设面积、绿地率、人均公共绿地面积、人均广场面积、人均商业设施用地面积、中小学人均用地面积达标率、百万人拥有图书馆等场所的个数等。根据各专项规划之间的协作与分工,本研究主要提出与宜居xx直接相关的核心指标。 就其本质而言,各项用地应保持合理比例,完善城市功能,美化城市环境,以达到宜居城市的标准。 (二)空间发展思路 1、城市功能联动尺度拓展的必要性 提升中心功能,实现增长与提升并重的全菜单功能完善是xx未来的主要方向。从传统商贸向现代服务的转型。培育科技生产力、文化影响力和对外交往能力,打造对接主城的渝东北连接一圈两翼的“桥头堡”功能。 2、构筑两基地一中心,促进整体联动发展 xx天然气精细化工基地:依托现有的xx化工、xx气田、xx厂等形成天然气精细化工的产业基地。 xx轻型加工制造业基地:依托xx的交通地理优势,打造生态型农产品加工基地,为县城和重庆主城建立生态农产品定点采购基地。 县城经济政治文化中心:依托城际铁路、高速公路等陆上交通,做强县城工业园区,培育现代物流中心,合理县城功能分区,构建经济文化中心。 3、城区空间构架

高级漂亮图表制作

高级漂亮图表制作 导语: 高级漂亮的图表总能让你的数据显得更加的专业,更加吸引目光。不熟悉Excel制作图表的朋友可能会认为Excel中图表都不是很好看。但是,如果经过一番美化后,那又是不同的效果了。这里小编给你分享一种简单、实用的数据图表制作方法,让你更轻松地制作出专业好看的图表。 免费获取商务图表软件:https://www.doczj.com/doc/3d2061769.html,/businessform/ 数据分析的过程和结果的呈现最直观的是采取图表的形式,图表直观有冲击力。只有正确地使用图表,才可以向业务方讲好数据故事,从而来支撑业务发展。 什么软件可以制作出高级漂亮的图表? 图表制作通常我们会使用Excel、Word制作,但一些稍复杂的图表使用这些软件制作便有些吃力,或者说又要借助其他软件才能绘制。亿图图示,一款专业绘制各类图表的软件。软件可以绘制柱状图、饼图、条形图、雷达图、气泡图等

一系列图表,操作使用简单,无需特意学习便可绘制,图种齐全,是绘制图表的不二选择。 使用亿图图示专家绘制图表有何优势? 1.可以通过模板、例子,快速创建出专业的图表; 2.图表数据可以进行实时修改; 3.自带多种主题、样式,可以一键更改整个主题风格; 4.可以通过导入数据快速创建图表; 5.可以将图表中的数据直接导出为Excel; 6.支持跨平台操作,可用于windows、Mmac以及 linux系统;

7.可以一键导入导出为visio,以及导出PDF、SVG、office、PowerPoint、 图片格式的文件; 8.支持云储存,个人云和团队云; 9.所见即所得的打印方式。 丰富的模板和例子:

商业空间设计研究报告

商业空间设计研究 空间概念: 空间的定义,用日本当代著名建筑师芦原义信的说法是:由一个物体同感受他的人之间产生的相互关系所形成。这一相互关系主要是视觉确定的,但作为建筑空间考虑时,则涵盖了视觉、嗅觉、听觉等其他感官。 “埏埴(shān zhí)以为器,当其无有器之用。凿户牖(you)以为室,当其无有室之用。是故有之以为利,无之以为用”。老子的这段话,也在芦原义信的《外部空间设计》一书中被引用,代表了东方文化对建筑空间的认识。捏土造器,其器的本质也不再是土,在它当中产生了“无”的空间。 空间是一种被限定的三维环境,是一个内空体,是可被感知的场所。而建筑空间则是提供人们各种具体的,特定的生活活动而人为限定的空间。限定建筑空间三要素:地板、墙壁、天花板。 建筑师通过地板、墙壁、天花板三者有机结合,创造出内部空间。可保护人不受自然威胁及外界侵扰,提供具有目的性或功能性的生活场所。而建筑的外部空间是通过地板或墙壁从自然中划定的空间。 商业空间: 商业空间设计不仅仅是建筑艺术的体现,也是承载着人们的使用功能和城市职能的公共产品。尤其是城市综合体或大型购物中心,其空间设计与消费者心理密不可分,人的心理现象多种多样,但归纳起来总的可分为个性的心理倾向性与个性的心理特征两大类。经营成功的商场,都是从顾客的需要和喜好出发,以顾客的导向性而设计。

勒?柯布西耶说住宅不是居住的机器,赖特说建筑应该是自然的,要成为自然的一部分。商业建筑的设计当然也应充分尊重商业使用功能与自然的天性,使建筑空间从堆砌的历史风格中解脱出来回归其几何秩序,通过立方、圆锥、球、圆柱等形态在光线下显示出伟大的基本形式。 商业空间影响要素:通过水平的地板、天花板,以及垂直的墙壁的围合成为内部空间,并赋予商业使用功能即为商业空间。商业空间通过这样的水平、垂直面的限定对商业空间产生影响。 商铺合理开间、进深、层高比值: 某购物中心铺位规划时,当店铺顶面天花板的宽D与高H的比值如下时,给人的感受不尽相同。 当H

商业空间设计的现状与趋势及潜力

商业空间设计的现状与趋势及潜力 逛街、购物一直被视为是非「正务」,也一直被认为是微不足道的小事。然而,随着经济与生活环境水准之提升,加以休闲时间之增加,尤其受欧美先进国家周休二日之影响,我国亦将真正实施周休二日制;届时,逛街、购物,从事初级产业以外之商业活动,已逐渐被视为生活中不可或缺缺之必要“活动”与动态元素。它可以是一种人文活动,也可以是商业活动,更可以被视为是一种艺术,或环境认知与教育之多向度活动。为此,如何在现有体制下提供一个合理、人性且有效率之卖场商业环境已是文明生活的一环了。 然而国外之商业行为发展也着实深切影响了卖场商业空间之发展与建设,大型购物中心(Shopping Mall),行人徒步购物街(Pedestrian Mall)以及各种大型量贩中心或如雨后春笋般成长的工商综合区或大型目的型渡假中心;逐一成为必须面对或熟悉之一种生活模式或空间型态,而我们应朝那一个方向发展呢 现象与趋势 (一)人性化.科技化 西门町的行人徒步街在十年前推动时仍需仰赖很多权力执法部门之强力介入,而在社区意识抬头,社区总体营造成为社会发展之正常过程后,各地的行人徒步街已有蔚然

成风,究其原因,与民族认知及生活水平价值观之提升有密切关系。是故,老街新生,如鹿港、大溪、三峡与莺歌等之主张强调地方化与人性化之商店街已在科技化之发展中逆向找到其复生之泉源。 科技化是另一类型之商业空间发展模式,如量贩超市、超级连锁卖场、百货公司,它主张以计算机效率管理来取代一对一之传统服务模式,如百货公司中的糖果专卖店(Sweet Cottage)与三峡老街店杂货店中之五彩糖球(金怡糖),似乎是两种截然不同之风味与购物商业体验。 (二)自然化.主题化 80年代几乎可说是台湾商业空间与经营模式之革命转型里程碑。混合使用,因地制宜或创造多样性之综合创意商场(如诚品书店、何嘉仁书店)又与主题商店形成鲜明对比-例如麦当劳、Hard Rock Cafe、热带雨林…等。而天母地区各种主题餐厅、个性商店一一反映出商业空间之发展,着实必须呼应当地市民之生活型态乃至生活价值观与生活理念。 (三)个性化.大众化 台北永康街之艺术性个性化主题商圈已不只是一种地标,也成为一种生活语汇,货品之多寡与否是不重要,购物行为与商业模式不只是金钱之交易,它却更深一层代表着人际关系追求时尚之代言者或是社会之一面镜子,当然其活力也在此。渔夫家饭、长春藤生活系列、商店、书店、艺品、创作坊乃至民艺品无一不代表着地区之自明性,宛如旧金山区之艺术家街坊。而另一方面,大众化餐饮、家具「营区」亦同时鲜炽地阐明另类消费者之需求。 (四)艺术化.生活化 受到经济成长之鼓舞,艺廊、画廊或创作坊已成为中产知识分子的另一种生活消费镜子,而生活化之花市、玉市或路旁之水果摊、盆栽绿道亦一一反映出传统生活型态与消费空间之互动性,在生活扎根方面,假日农场、节庆市场乃至黄昏市集更一一代表着市民生活欢乐。情感之宣泄,而两者间之共存更说明了生活多样化之趋势与发展自主性。 (五)国际化.风土化

EXCEL怎么制作漂亮的柱状图

柱状图是经常需要绘制的图,本文讲解如何在软件绘制的默认的柱状图的基础上对柱状图进行美化设计,得到一张漂亮的柱状图。 1、录入数据。 2、选择数据,插入柱状图,得到软件默认的柱状图。 3、设计标题。在图表工具中选择图表标题;图表上方,然后输入需要的标题文字。设置标题字体和字号。选用笔划较粗的字体,使用大字号。 4、设计图例。选择在顶部显示图例,设置图例字体和字号。 5、设计柱形填充颜色。点选相同系列柱形,在图表工具;格式;形状填充中选择颜色。 6、设计数据标签。每一张图都有想说明的重点,所以不必将每个系列的数据标签都显示出,本例显示出第二系列的数据标签。选择数据标签外,显示出数据标签,并对数据设置字体和字号,并选用与系列柱形相同的颜色。 7、设计坐标轴。为了强化逐渐增加的趋势,可以将纵坐标轴的最大和最小刻度进行更改。因为已经显示出了数据标签,所以没必要再需要纵坐标轴,点击选择纵坐标轴,将其删除。点选横坐标轴,更改字体和字号。 8、设计网格线。无需网格线,将其删除。 9、整体再设计。这一步需要具体问题具体分析。本图因为是逐渐增加的风格,所以需要强化这个增加的趋势。把标题更改为居中覆盖标题,那么柱形将进一步扩大。同时手动将标题和图例移动到左边的空白区域,使整个图更显稳重。 10、增加对图形的说明。因为绘制图形的目的就是为了说明图形所要表达的意义,充分利用左边的空白区域,添加本图需要表达的意义。 11、设计柱形之间的间隔距离。双击柱形,弹出格式设置对话框,拖动滑块改变系类重

叠和分类间距的值。 12、再为图增加一个边框。在格式;形状轮廓中选择颜色,选择边框的粗细和线型。可以适当宽点。得到最终美化的柱形图。 注意事项:整体上保持简约,颜色不宜过分鲜艳,不宜太过花哨。 .. ;.

城市空间布局现状与未来趋势探讨

城市空间布局现状与未来趋势探讨 《人民论坛》<2018年第2期)魏广龙任登军 【摘要】改革开放以来,我国经济迅速发展,城市得到了前所未有地发展,但也带来了诸多问题.文章在介绍国外城市发展理论和研究方向地基础上分析了国内城市空间布局发展现状,找出我国城市空间布局地问题和缺陷,并提出改善城市空间布局地方法,即:资源分配均衡化、交通设计合理化、生态环境可持续化和土地利用集约化. 【关键词】城市发展理论城市空间布局均衡可持续集约 当前,我国正处于一个经济、社会地转型期,城市化速度不断加快,城市不断蔓延扩展,城市发展日新月异,但在经济利益驱动和地区发展不均衡等多种因素作用下,城市空间布局呈现出一些不合理地状况.大城市空间布局呈现两极分化地趋势,如:中心区空间布局过于拥挤,而城市外围地空间布局过于分散;产业区域相对密集,城市绿化率不断下降;等等.这些不合理地城市空间布局最终会影响人居环境质量下降,导致一系列严重地城市问题. 国外近代以来城市发展理论概述 城市疏散理论,即田园城市—卧城—卫星城理论.①19世纪末,英国社会活动家霍华德提出“田园城市”理论.在此基础上,1922年雷蒙恩·温提出“卫星城市”地概念.20世纪40年代,

处于二战后地修复时期,大城市改建时进行了第一代新城建设;50年代进行了第二代新城建设;60年代进行了第三代新城建设.20年代地卫星城概念只能称为城市郊区,第一代新城是卧城,由于规模过小,没有起到疏散地作用.第二代新城也属于卧城,依赖于大城市地发展,没有吸引力,因此起不到疏散城市地作用.第三代为独立地卫星城,有自己地吸引力,对城市疏散和容纳外来人口起到了一定地作用. 区域规划理论,即中心地理论—增长极核理论.②50年代,许多国家都进行了区域规划活动,提出了中心地理论,并在此基础上发展出增长极核理论,主张先促进少数经济上增长较快地城市迅速繁荣发展,进而带动附近区域地发展.此理论可促进城市快速发展,但容易引起城市地盲目蔓延. 城市美化运动,即城市环境生态学.③二战前期,在美国,先驱者们主张人与自然要正确合作,美国许多城市进行了公共绿地规划.自20年代至今,一直注重城市环境生态学对城市规划地影响,强调将自然环境和城市作为一个整体进行设计. 城市发展由单一中心城市到多中心城市再到大都市绵延区.④二十世纪六七十年代,世界城市化仍在继续,大多数国家开始控制大城市,发展中小城市.大城市地布局由单一中心城市模式转为多中心城市模式.单一中心城市造成城市无限蔓延,多中心城市郊区化现象严重,逐渐发展为大城市绵延区,形成无边地城市,造成土地大面积浪费.

浅析中国酒店空间设计现状及未来发展方向

浅析中国酒店设计现状及未来发展方向 酒店是一个国家或一个城市文化的延伸,也是一个国家或城市的缩影。酒店的室内设计充分体现一个酒店的档次、文化、定位、目标客户群等。如今,越来越多的设计师认识到酒店室内设计的重要性,逐渐更多地参与到酒店的设计中。正因为这样,今后的酒店,不只是单纯的“设计”,它将酒店结合文化概念,从空间规划建筑架构的角度,构思关于旅行居住及生活形式的设计,当设计成为酒店的关键及优势所在时,高档酒店的设计已不再只以呈现豪华、气派为目标,而是更贴近人的生活去思考。 一、中国酒店设计现状 中国酒店室内设计真正开始于二十世纪八十年代,发展至今已有二十几年历史。在这段历程中,国内酒店设计行业的水平有了很大提高,但还有诸多不尽人意的地方,主要表现在以下几方面: 1、设计模式化,缺乏创造力和特色。 走遍中国各地酒店,人们会发现这么多酒店在项目规划、设计风格与手法、材料乃至平面布置上都是那么相似。造成这种现象的原因是多方面的,有的是因为设计时间紧迫而轻率的“拿来主义”,有的则是因为设计单位缺乏经验而不得不效仿现有的其他项目设计。酒店的客房装修设计更是惊人地相似,或许是因受到星级酒店评定的约束。 但事实上,因为每家酒店所处地理环境都是不同的,业主投资及经营的定位也各不相同,这种千篇一律、大量复制的设计现状是极不科学的。 2、重视空间硬装饰,配饰及艺术陈设品设计却相对落后。 正因为目前国内酒店设计还处于初级阶段,所以各设计单位往往将绝大部分精力放在传统的界面装饰上,对于灯具、家具、艺术陈设品这些“小品”普遍重视不足。事实上,随着经济水平的提高和人们见闻的扩展,客人对入住酒店的要求已远远超出了基本的住宿、餐饮功能,除此之外,还对酒店的会议、商务、娱乐、健身及艺术氛围等功能与环境要素怀有更高的期望。 3、重大堂设计,轻客房设计。 这种现象的出现也源于设计师没有真正的做到以人为本。大堂设计固然重要,因为这关系到客人对酒店的第一印象。但客人入住酒店后,大部分时间是在客房内度过的,而且客房才是酒店创造效益的主要部分,因此客房设计的合理与否与客人的满意度乃至酒店的效益盈亏都有紧密的联系。 如今,国内酒店设计师从最初的简单模仿和抄袭渐渐的成熟起来,有的设计师还开始重点研究母体文化,探索具有民族特色的设计手法,这些都是值得我们欣喜的。但我们也不得不看到,由于国内酒店设计还存在包括以上所列的各种不足,我们的设计还不能真正满足业主的要求,所以国内目前最高档次的酒店设计还是被国外设计公司所垄断。我们应从中借鉴先进的设计思想,早日探索出一条符合中国特色的酒店设计之路。 二、国际酒店设计的发展趋势 目前,世界范围内新一轮酒店设计已经开始,纽约、东京、巴黎、香港、伦敦、上海等国际性大都市近期都涌现出新时期引领潮流的五星级酒店,虽然隶属不同的酒店管理公司,不同的地点,不同的规模,但它们都呈现出一些共同点,那就是越来越多元化、个性化、全面化。 这种发展趋势告诉我们,今天的酒店已不是单一的豪华型,主题性、精品类、设计型的酒店已越来越被人们接受。强调个性、追求新颖、突破传统,已成为设计的目标。集吃、住、会议、娱乐、休闲多功能于一体的全方位、全面化的复合型酒店也有了广阔的市场。 今后的酒店,不只是单纯的“设计”,它将酒店结合文化概念,从空间规划建筑架构的角度,构思关于旅行居住及生活形式的设计,当设计成为酒店的关键及优势所在时,高档酒店的设计已不再只以呈现豪华、气派为目标,而是更贴近人的生活去思考。它要求设计更加迎合客人心理需求。客人步入酒店时就会感到一种温暖、舒适和倍受欢迎的氛围;同时,设计更加体现地域文化。同一品牌的酒店在不同地区、不同文化背景下,采用了不同的设计,以体现出地

相关主题
相关文档 最新文档