Bayesian analysis for the social sciences
著者
書誌事項
Bayesian analysis for the social sciences
(Wiley series in probability and mathematical statistics)
Wiley, 2009
大学図書館所蔵 全30件
  青森
  岩手
  宮城
  秋田
  山形
  福島
  茨城
  栃木
  群馬
  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
  中国
  タイ
  イギリス
  ドイツ
  スイス
  フランス
  ベルギー
  オランダ
  スウェーデン
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  アメリカ
注記
Includes bibliographical references (p. [535]-552) and indexes
内容説明・目次
内容説明
Bayesian methods are increasingly being used in the social sciences, as the problems encountered lend themselves so naturally to the subjective qualities of Bayesian methodology. This book provides an accessible introduction to Bayesian methods, tailored specifically for social science students. It contains lots of real examples from political science, psychology, sociology, and economics, exercises in all chapters, and detailed descriptions of all the key concepts, without assuming any background in statistics beyond a first course. It features examples of how to implement the methods using WinBUGS - the most-widely used Bayesian analysis software in the world - and R - an open-source statistical software. The book is supported by a Website featuring WinBUGS and R code, and data sets.
目次
List of Figures. List of Tables.
Preface.
Acknowledgments.
Introduction.
Part I: Introducing Bayesian Analysis.
1. The foundations of Bayesian inference.
1.1 What is probability?
1.2 Subjective probability in Bayesian statistics.
1.3 Bayes theorem, discrete case.
1.4 Bayes theorem, continuous parameter.
1.5 Parameters as random variables, beliefs as distributions.
1.6 Communicating the results of a Bayesian analysis.
1.7 Asymptotic properties of posterior distributions.
1.8 Bayesian hypothesis testing.
1.9 From subjective beliefs to parameters and models.
1.10 Historical note.
2. Getting started: Bayesian analysis for simple models.
2.1 Learning about probabilities, rates and proportions.
2.2 Associations between binary variables.
2.3 Learning from counts.
2.4 Learning about a normal mean and variance.
2.5 Regression models.
2.6 Further reading.
Part II: Simulation Based Bayesian Analysis.
3. Monte Carlo methods.
3.1 Simulation consistency.
3.2 Inference for functions of parameters.
3.3 Marginalization via Monte Carlo integration.
3.4 Sampling algorithms.
3.5 Further reading.
4. Markov chains.
4.1 Notation and definitions.
4.2 Properties of Markov chains.
4.3 Convergence of Markov chains.
4.4 Limit theorems for Markov chains.
4.5 Further reading.
5. Markov chain Monte Carlo.
5.1 Metropolis-Hastings algorithm.
5.2 Gibbs sampling.
6. Implementing Markov chain Monte Carlo.
6.1 Software for Markov chain Monte Carlo.
6.2 Assessing convergence and run-length.
6.3 Working with BUGS/JAGS from R.
6.4 Tricks of the trade.
6.5 Other examples.
6.6 Further reading.
Part III: Advanced Applications in the Social Sciences.
7. Hierarchical Statistical Models.
7.1 Data and parameters that vary by groups: the case for hierarchical modeling.
7.2 ANOVA as a hierarchical model.
7.3 Hierarchical models for longitudinal data.
7.4 Hierarchical models for non-normal data.
7.5 Multi-level models.
8. Bayesian analysis of choice making.
8.1 Regression models for binary responses.
8.2 Ordered outcomes.
8.3 Multinomial outcomes.
8.4 Multinomial probit.
9. Bayesian approaches to measurement.
9.1 Bayesian inference for latent states.
9.2 Factor analysis.
9.3 Item-response models.
9.4 Dynamic measurement models.
Part IV: Appendices.
Appendix A: Working with vectors and matrices.
Appendix B: Probability review.
B.1 Foundations of probability.
B.2 Probability densities and mass functions.
B.3 Convergence of sequences of random variabales.
Appendix C: Proofs of selected propositions.
C.1 Products of normal densities.
C.2 Conjugate analysis of normal data.
C.3 Asymptotic normality of the posterior density.
References.
Topic index.
Author index.
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