From the Publisher:
Probabilistic Reasoning in Intelligent Systems is a complete andaccessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic. The author distinguishes syntactic and semantic approaches to uncertaintyand offers techniques, based on belief networks, that provide a mechanism for making semantics-based systems operational. Specifically, network-propagation techniques serve as a mechanism for combining the theoretical coherence of probability theory with modern demands of reasoning-systems technology: modular declarative inputs, conceptually meaningful inferences, and parallel distributed computation. Application areas include diagnosis, forecasting, image interpretation, multi-sensor fusion, decision support systems, plan recognition, planning, speech recognitionin short, almost every task requiring that conclusions be drawn from uncertain clues and incomplete information.
Probabilistic Reasoning in Intelligent Systems will be of special interest to scholars and researchers in AI, decision theory, statistics, logic, philosophy, cognitive psychology, and the management sciences. Professionals in the areas of knowledge-based systems, operations research, engineering, and statistics will find theoretical and computational tools of immediate practical use. The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.

Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference

计量经济分析 = Econometric analysis

This article is a review of the literature on classroom formative assessment. Several studies show firm evidence that innovations designed to strengthen the frequent feedback that students receive about their learning yield substantial learning gains. The perceptions of students and their role in self‐assessment are considered alongside analysis of the strategies used by teachers and the formative strategies incorporated in such systemic approaches as mastery learning. There follows a more detailed and theoretical analysis of the nature of feedback, which provides a basis for a discussion of the development of theoretical models for formative assessment and of the prospects for the improvement of practice.

Assessment and Classroom Learning

I hope that some people see some connection between the two topics in the title. If not, anyway, such connections will be developed in the course of these talks. Furthermore, because of the use of tools involving reference and necessity in analytic philosophy today, our views on these topics really have wide-ranging implications for other problems in philosophy that traditionally might be thought far-removed, like arguments over the mind-body problem or the so-called ‘identity thesis’. Materialism, in this form, often now gets involved in very intricate ways in questions about what is necessary or contingent in identity of properties — questions like that. So, it is really very important to philosophers who may want to work in many domains to get clear about these concepts. Maybe I will say something about the mind-body problem in the course of these talks. I want to talk also at some point (I don’t know if I can get it in) about substances and natural kinds.

Naming and Necessity

Contents Dedication Preface Acknowledgements Notation A general classification notation and diagram Glossary Chapter 1 An introduction to multilevel models 1.1 Hierarchically structured data 1.2 School effectiveness 1.3 Sample survey methods 1.4 Repeated measures data 1.5 Event history and survival models 1.6 Discrete response data 1.7 Multivariate models 1.8 Nonlinear models 1.9 Measurement errors 1.10 Cross classifications and multiple membership structures. 1.11 Factor analysis and structural equation models 1.12 Levels of aggregation and ecological fallacies 1.13 Causality 1.14 The latent normal transformation and missing data 1.15 Other texts 1.16 A caveat Chapter 2 The 2-level model 2.1 Introduction 2.2 The 2-level model 2.3 Parameter estimation 2.4 Maximum likelihood estimation using Iterative Generalised Least Squares (IGLS) 2.5 Marginal models and Generalized Estimating Equations (GEE) 2.6 Residuals 2.7 The adequacy of Ordinary Least Squares estimates. 2.8 A 2-level example using longitudinal educational achievement data 2.9 General model diagnostics 2.10 Higher level explanatory variables and compositional effects 2.11 Transforming to normality 2.12 Hypothesis testing and confidence intervals 2.13 Bayesian estimation using Markov Chain Monte Carlo (MCMC) 2.14 Data augmentation Appendix 2.1 The general structure and maximum likelihood estimation for a multilevel model Appendix 2.2 Multilevel residuals estimation Appendix 2.3 Estimation using profile and extended likelihood Appendix 2.4 The EM algorithm Appendix 2.5 MCMC sampling Chapter 3. Three level models and more complex hierarchical structures. 3.1 Complex variance structures 3.2 A 3-level complex variation model example. 3.3 Parameter Constraints 3.4 Weighting units 3.5 Robust (Sandwich) Estimators and Jacknifing 3.6 The bootstrap 3.7 Aggregate level analyses 3.8 Meta analysis 3.9 Design issues Chapter 4. Multilevel Models for discrete response data 4.1 Generalised linear models 4.2 Proportions as responses 4.3 Examples 4.4 Models for multiple response categories 4.5 Models for counts 4.6 Mixed discrete - continuous response models 4.7 A latent normal model for binary responses 4.8 Partitioning variation in discrete response models Appendix 4.1. Generalised linear model estimation Appendix 4.2 Maximum likelihood estimation for generalised linear models Appendix 4.3 MCMC estimation for generalised linear models Appendix 4.4. Bootstrap estimation for generalised linear models Chapter 5. Models for repeated measures data 5.1 Repeated measures data 5.2 A 2-level repeated measures model 5.3 A polynomial model example for adolescent growth and the prediction of adult height 5.4 Modelling an autocorrelation structure at level 1. 5.5 A growth model with autocorrelated residuals 5.6 Multivariate repeated measures models 5.7 Scaling across time 5.8 Cross-over designs 5.9 Missing data 5.10 Longitudinal discrete response data Chapter 6. Multivariate multilevel data 6.1 Introduction 6.2 The basic 2-level multivariate model 6.3 Rotation Designs 6.4 A rotation design example using Science test scores 6.5 Informative response selection: subject choice in examinations 6.6 Multivariate structures at higher levels and future predictions 6.7 Multivariate responses at several levels 6.8 Principal Components analysis Appendix 6.1 MCMC algorithm for a multivariate normal response model with constraints Chapter 7. Latent normal models for multivariate data 7.1 The normal multilevel multivariate model 7.2 Sampling binary responses 7.3 Sampling ordered categorical responses 7.4 Sampling unordered categorical responses 7.5 Sampling count data 7.6 Sampling continuous non-normal data 7.7 Sampling the level 1 and level 2 covariance matrices 7.8 Model fit 7.9 Partially ordered data 7.10 Hybrid normal/ordered variables 7.11 Discussion Chapter 8. Multilevel factor analysis, structural equation and mixture models 8.1 A 2-stage 2-level factor model 8.2 A general multilevel factor model 8.3 MCMC estimation for the factor model 8.4 Structural equation models 8.5 Discrete response multilevel structural equation models 8.6 More complex hierarchical latent variable models 8.7 Multilevel mixture models Chapter 9. Nonlinear multilevel models 9.1 Introduction 9.2 Nonlinear functions of linear components 9.3 Estimating population means 9.4 Nonlinear functions for variances and covariances 9.5 Examples of nonlinear growth and nonlinear level 1 variance Appendix 9.1 Nonlinear model estimation Chapter 10. Multilevel modelling in sample surveys 10.1 Sample survey structures 10.2 Population structures 10.3 Small area estimation Chapter 11 Multilevel event history and survival models 11.1 Introduction 11.2 Censoring 11.3 Hazard and survival funtions 11.4 Parametric proportional hazard models 11.5 The semiparametric Cox model 11.6 Tied observations 11.7 Repeated events proportional hazard models 11.8 Example using birth interval data 11.9 Log duration models 11.10 Examples with birth interval data and children s activity episodes 11.11 The grouped discrete time hazards model 11.12 Discrete time latent normal event history models Chapter 12. Cross classified data structures 12.1 Random cross classifications 12.2 A basic cross classified model 12.3 Examination results for a cross classification of schools 12.4 Interactions in cross classifications 12.5 Cross classifications with one unit per cell 12.6 Multivariate cross classified models 12.7 A general notation for cross classifications 12.8 MCMC estimation in cross classified models Appendix 12.1 IGLS Estimation for cross classified data. Chapter 13 Multiple membership models 13.1 Multiple membership structures 13.2 Notation and classifications for multiple membership structures 13.3 An example of salmonella infection 13.4 A repeated measures multiple membership model 13.5 Individuals as higher level units 13.5.1 Example of research grant awards 13.6 Spatial models 13.7 Missing identification models Appendix 13.1 MCMC estimation for multiple membership models. Chapter 14 Measurement errors in multilevel models 14.1 A basic measurement error model 14.2 Moment based estimators 14.3 A 2-level example with measurement error at both levels. 14.4 Multivariate responses 14.5 Nonlinear models 14.6 Measurement errors for discrete explanatory variables 14.7 MCMC estimation for measurement error models Appendix 14.1 Measurement error estimation 14.2 MCMC estimation for measurement error models Chapter 15. Smoothing models for multilevel data. 15.1 Introduction 15.2. Smoothing estimators 15.3 Smoothing splines 15.4 Semi parametric smoothing models 15.5 Multilevel smoothing models 15.6 General multilevel semi-parametric smoothing models 15.7 Generalised linear models 15.8 An example Fixed Random 15.9 Conclusions Chapter 16. Missing data, partially observed data and multiple imputation 16.1 Creating a completed data set 16.2 Joint modelling for missing data 16.3 A two level model with responses of different types at both levels. 16.4 Multiple imputation 16.5 A simulation example of multiple imputation for missing data 16.6 Longitudinal data with attrition 16.7 Partially known data values 16.8 Conclusions Chapter 17 Multilevel models with correlated random effects 17.1 Non-independence of level 2 residuals 17.2 MCMC estimation for non-independent level 2 residuals 17.3 Adaptive proposal distributions in MCMC estimation 17.4 MCMC estimation for non-independent level 1 residuals 17.5 Modelling the level 1 variance as a function of explanatory variables with random effects 17.6 Discrete responses with correlated random effects 17.7 Calculating the DIC statistic 17.8 A growth data set 17.9 Conclusions Chapter 18. Software for multilevel modelling References Author index Subject index

Multilevel Statistical Models

Preface. 1. A Philosopher's Paradise. The Thesis of Pluraliry of Worlds. Modal Realism at Work: Modality. Modal Realism at Work: Closeness. Modal Realism at Work: Content. Modal Realism at Work: Properties. Isolation. Concreteness. Plenitude. Actuality. 2. Paradox in Paradise? Everything is Actual? All Worlds in One? More Worlds Than There Are? How Can We Know? A Road to Scepticism? A Road to Indifference? Arbitrariness Lost? The Incredulous Stare. 3. Paradise on the Cheap? The Ersatzist Program. Linguistic Ersatzism. Pictorial Ersatzism. Magical Ersatzism. 4. Counterparts or Double Lives? Good Questions and Bad. Against Overlap. Against Trans-World Individuals. Against Haecceitism. Against Constancy. Works Cited. Index.

On the Plurality of Worlds

Acknowledgements. Foreword by W.V. Quine. Introduction. I. Coordination and Convention. Sample Coordination Problems. Analysis of Coordination Problems. Solving Coordination Problems. Convention. Sample Conventions. II. Convention Refined. Common Knowledge. Knowledge of Conventions. Alternatives to Convention. Degrees of Convention. Consequences of Conventions. III. Convention Contrasted. Agreement. Social Contracts. Norms. Rules. Conformative Behavior. Imitation. Meaning of Signals. IV. Convention and Communication. Sample Signals. Analysis of Signaling. Verbal Signaling. Conventional Meaning of Signals. V. Conventions of Language. Possible Languages. Grammars. Semantics in a Possible Language. Conventions of Truthfulness. Semantics in a Population. Conclusion. Index.

Convention: A Philosophical Study

David Lewis is one of the most influential philosophers of our age, and On the Plurality of Worlds is his magnum opus. OPW offers an extended development and defense of the hypothesis that there are many universes, things of the same kind as the universe in which we all live, move, and have our being. Lewis calls these universes ―worlds‖, deliberately recalling the notion of a ―possible world‖ familiar from modal logic and the metaphysics of modality. The title invokes the thesis of the book: there are pluralities of worlds, things of the same kind as the world we inhabit, differing only with respect to what goes on in them. Lewis sought in earlier work (Lewis, 1973, pp. 84–86) to offer a direct argument from common sense modal commitments to the existence of a plurality of worlds. OPW offers a less direct argument. Here, Lewis supports the hypothesis by arguing that, if we accept it, we have the material to offer a wide range of analyses of hitherto puzzling and problematic notions. We thereby effect a theoretical unification and simplification: with a small stock of primitives, we can analyze a number of important philosophical notions with a broad range of applications. But the analyses Lewis proposes are adequate only if we accept the thesis that there are a plurality of worlds. Lewis claims that this is a reason to accept the thesis. In his words, «the hypothesis is serviceable, and that is a reason to think that it is true» (p. 3).

EXAMPLE 1: PRESUPPOSITION’ At any stage in a well-run conversation, a certain amount is presupposed. The parties to the conversation take it for granted; or at least they purport to, whether sincerely or just “for the sake of the argument”. Presuppositions can be created or destroyed in the course of a conversation. This change is rule-governed, at least up to a point. The presuppositions at time r’ depend, in a way about which at least some general principles can be laid down, on the presuppositions at an earlier time r and on the course of the conversation (and nearby events) between r and r’. Some things that might be said require suitable presuppositions. They are acceptable if the required presuppositions are present; not otherwise. ‘me king of France is bald” requires the presupposition that France has one king, and one only; “Even George Lakoff could win” requires the presupposition that George is not a leading candidate; and so on. We need not ask just what sort of unacceptability results when a required presupposition is lacking. Some say falsehood, some say lack of truth value, some just say that it’s the kind of unacceptability that results when a required presupposition is 1acking;and some say it might vary from case to case. Be that as it may, it’s not as easy as you might think to say something that will be unacceptable for lack of required presuppositions. Say something that requires a missing presupposition, and straightway that presupposition springs into existence, making what you said acceptable after all. (Or at least, that is what happens if your conversational partners tacitly acquiesce - if no one says “But France has three kings! ” or ‘Vhadda ya mean, ‘even George’? “) That is why it is peculiar to say, out of the blue, “All Fred’s children are asleep, and Fred has children.” The first part requires and thereby creates a presupposition that Fred has children; so the second part adds nothing to what is already presupposed when it is said; so the second part has no conversational point. It would not have been peculiar to say instead “Fred has children, and all Fred’s children are asleep.”

https://semantics.uchicago.edu/kennedy/classes/s06/readings/lewis79.pdf

Scorekeeping in a Language Game

(1983). New work for a theory of universals. Australasian Journal of Philosophy: Vol. 61, No. 4, pp. 343-377.

David Lewis

Papers

On the Plurality of Worlds

Convention: A Philosophical Study

On the Plurality of Worlds

Scorekeeping in a Language Game

New work for a theory of universals