貝葉斯方法：英文版

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