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贝叶斯方法：英文版
作者：（美）Thomas Leonard，（美）John S.J.Hsu著
出版社：机械工业出版社
出版时间：2005-01-01
ISBN：9787111158325
定价：¥45.00
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内容简介
“本书提供了有关最新现代贝叶斯统计方法的重要题材，文笔流畅，语言优美，其突出的特点是包括大量实际应用，涉及若干领域中AIC和BIC模型选择标准的运用和对比，通过效用理论以独特方式处理贝叶斯决策论，并论述了贝叶斯过程的频度特性，配备了可以扩展与加深书中内容的有趣和适中的自学练习。”——Michael J．Evans，Mathematical Review“以严密、纯熟的文笔介绍贝叶斯建模的基本原则，选材深思熟虑，按照研究生层次引入贝叶斯方法。”——Journal of the American Statistical Association贝叶斯“后验分布”或“预测分布”是对有关未知参或未来观测所需了解的每项事物的概括。本书以一种强有力和贴切的方式说明了如何运用贝叶斯统计技术，引导读者从具体数据中推测有关科学、医疗与社会问题的结论。本书解释了贝叶斯方法论所需的一些细微假设，并展示了如何运用这些假设去获取准确结论。本书所介绍的各种方法对计算机模拟的频度特性方面也非常适用。本书生动地概述了有关费希尔方法（频度方法），同时全面强调了似然性，适合作为主流统计学的教程。本书讲述了效用理论的进展以及时间序列和预测，从而也适合计量经济学的学生阅读。另外，本书还包括线性模型、范畴数据分析、生存竞争分析、随机效应模型和非线性平滑等内容。本书提供了许多运行实例、自学练习和实际应用，可作为高年级本科生和研究生的教材，同时也可供其他交叉学科的研究人员阅读。
作者简介
Thomas Leonard 于1973年在伦敦大学获得统计学博士学位。他曾在沃里克大学工作过，于1995年担任爱丁堡大学统计学系主席，还曾做过威斯康星-麦迪逊大学统计学教授。20世纪80年代，他最早将拉普斯算子引入到贝叶斯方法中。他发表了多篇有关统计学应用方面的论文，并作为统计学专家参与过多个美国法律诉讼案件。John S.J.Hsu 加州大学圣芭芭拉分校统计学与应用概率论副教授、统计实验室主任，擅长研究应用问题，还建立了贝叶斯理论研究计划。由于在log-线性模型分析方面的贡献，他获得了爱丁堡大学的名誉职位。在Thomas Leonard和Kam-Wah Tsui的指导下，他于1990年在威斯康星-麦迪逊大学获得统计学博士学位。
目录
1 Introductory Statistical Concepts
1.0 Preliminaries and Overview
1.1 Sampling Models and Likelihoods
1.2 Practical Examples
1.3 Large Sample Properties of Likelihood Procedures
1.4 Practical Examples
1.5 Some Further Properties of Likelihood
1.6 Practical Examples
1.7 The Midcontinental Rift
1.8 A Model for Genetic Traits in Dairy Science
1.9 Least Squares Regression with Serially Correlated Errors
1.10 Annual World Crude Oil Production (1880-1972)2 The Discrete Version of Bayes' Theorem
2.0 .preliminaries and Overview
2.1 Bayes' Theorem
2.2 Estimating a Discrete-Valued Parameter
2.3 Applications to Model Selection
2.4 Practical Examples
2.5 Logistic Discrimination and the Construction of Neural Nets
2.6 Anderson's Prediction of Psychotic Patients
2.7 The Ontario Fetal Metabolic Acidosis Study
2.8 Practical Guidelines
3 Models with a Single Unknown Parameter
3.0 Preliminaries and Overview
3.1 The Bayesian Paradigm
3.2 Posterior and Predictive Inferences
3.3 Practical Examples
3.4 Inferences for a Normal Mean with Known Variance
3.5 Practical Examples
3.6 Vague Prior Information
3.7 Practical Examples
3.8 Bayes Estimators and Decision Rules and Their
Frequency Properties
3.9 Practical Examples
3.10 Symmetric Loss Functions
3.11 Practical Example: Mixtures of Normal Distributions
4 The Expected Utility Hypothesis
4.0 Preliminaries and Overview
4.1 Classical Theory
4.2 The Savage Axioms
4.3 Modifications to the Expected Utility Hypothesis
4.4 The Experimental Measurement of 6-Adjusted Utility
4.5 The Risk-Aversion Paradox
4.6 The Ellsberg Paradox
4.7 A Practical Case Study
5 Models with Several Unknown Parameters
5.0 Preliminaries and Overview
5.1 Bayesian Marginalization
5.2 Further Methods and Practical Examples
5.3 The Kalman Filter
5.4 An On-Line Analysis of Chemical Process Readings
5.5 An Industrial Control Chart
5.6 Forecasting Geographical Proportions for World Sales of Fibers
5.7 Bayesian Forecasting in Economics
6 Prior Structures, Posterior Smoothing, and Bayes-Stein Estimation
6.0 Preliminaries and Overview
6.1 Multivariate Normal Priors for the Transformed Parameters
6.2 Posterior Mode Vectors and Laplacian Approximations
6.3 Prior Structures, and Modeling for Nonrandomized Data
6.4 Monte Carlo Methods and Importance Sampling
6.5 Further Special Cases and Practical Examples
6.6 Markov Chain Monte Carlo (MCMC) Methods:The Gibbs Sampler
6.7 Modeling Sampling Distributions, Using MCMC
6.8 Equally Weighted Mixtures and Survivor Functions
6.9 A Hierarchical Bayes Analysis
References
Author Index
Subject Index
1.0 Preliminaries and Overview
1.1 Sampling Models and Likelihoods
1.2 Practical Examples
1.3 Large Sample Properties of Likelihood Procedures
1.4 Practical Examples
1.5 Some Further Properties of Likelihood
1.6 Practical Examples
1.7 The Midcontinental Rift
1.8 A Model for Genetic Traits in Dairy Science
1.9 Least Squares Regression with Serially Correlated Errors
1.10 Annual World Crude Oil Production (1880-1972)2 The Discrete Version of Bayes' Theorem
2.0 .preliminaries and Overview
2.1 Bayes' Theorem
2.2 Estimating a Discrete-Valued Parameter
2.3 Applications to Model Selection
2.4 Practical Examples
2.5 Logistic Discrimination and the Construction of Neural Nets
2.6 Anderson's Prediction of Psychotic Patients
2.7 The Ontario Fetal Metabolic Acidosis Study
2.8 Practical Guidelines
3 Models with a Single Unknown Parameter
3.0 Preliminaries and Overview
3.1 The Bayesian Paradigm
3.2 Posterior and Predictive Inferences
3.3 Practical Examples
3.4 Inferences for a Normal Mean with Known Variance
3.5 Practical Examples
3.6 Vague Prior Information
3.7 Practical Examples
3.8 Bayes Estimators and Decision Rules and Their
Frequency Properties
3.9 Practical Examples
3.10 Symmetric Loss Functions
3.11 Practical Example: Mixtures of Normal Distributions
4 The Expected Utility Hypothesis
4.0 Preliminaries and Overview
4.1 Classical Theory
4.2 The Savage Axioms
4.3 Modifications to the Expected Utility Hypothesis
4.4 The Experimental Measurement of 6-Adjusted Utility
4.5 The Risk-Aversion Paradox
4.6 The Ellsberg Paradox
4.7 A Practical Case Study
5 Models with Several Unknown Parameters
5.0 Preliminaries and Overview
5.1 Bayesian Marginalization
5.2 Further Methods and Practical Examples
5.3 The Kalman Filter
5.4 An On-Line Analysis of Chemical Process Readings
5.5 An Industrial Control Chart
5.6 Forecasting Geographical Proportions for World Sales of Fibers
5.7 Bayesian Forecasting in Economics
6 Prior Structures, Posterior Smoothing, and Bayes-Stein Estimation
6.0 Preliminaries and Overview
6.1 Multivariate Normal Priors for the Transformed Parameters
6.2 Posterior Mode Vectors and Laplacian Approximations
6.3 Prior Structures, and Modeling for Nonrandomized Data
6.4 Monte Carlo Methods and Importance Sampling
6.5 Further Special Cases and Practical Examples
6.6 Markov Chain Monte Carlo (MCMC) Methods:The Gibbs Sampler
6.7 Modeling Sampling Distributions, Using MCMC
6.8 Equally Weighted Mixtures and Survivor Functions
6.9 A Hierarchical Bayes Analysis
References
Author Index
Subject Index
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