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。
Preface
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(1880-0972)
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
……
5 Models with Several Unknown Parameters
6 Prior Structures,Posterior Smoothing,and Bayes-Stein Estimation
References
Author Index
Subject Index