Bayesian Data Analysis
Bayesian Data Analysis
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DOI：
10.4324/9780203491287
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发表时间：
1995
期刊：
影响因子：
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通讯作者：
Andrew Gelman;John B. Carlin;H. Stern;David B. Dunson;A. Vehtari;Donald B. Rubin
中科院分区：
文献类型：
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作者：
Andrew Gelman;John B. Carlin;H. Stern;David B. Dunson;A. Vehtari;Donald B. Rubin
贝叶斯推理的基本原理概率和推理单参数模型介绍多参数模型渐近和连接到非贝叶斯方法分层模型贝叶斯数据分析的基本原理模型检查评估，比较，和扩展模型建模考虑数据收集决策分析高级解释介绍贝叶斯计算马尔可夫链仿真的基础计算效率高的马尔可夫链仿真模型和分布近似回归模型回归模型简介分层线性模型广义线性模型缺失数据的稳健推断模型非线性和非参数模型参数非线性模型基本函数模型高斯过程模型有限混合模型Dirichlet过程模型附录A：标准概率分布B：渐近定理证明大纲C：在R计算和斯坦书目注释和练习出现在每章的末尾。
FUNDAMENTALS OF BAYESIAN INFERENCE Probability and Inference Single-Parameter Models Introduction to Multiparameter Models Asymptotics and Connections to Non-Bayesian Approaches Hierarchical Models FUNDAMENTALS OF BAYESIAN DATA ANALYSIS Model Checking Evaluating, Comparing, and Expanding Models Modeling Accounting for Data Collection Decision Analysis ADVANCED COMPUTATION Introduction to Bayesian Computation Basics of Markov Chain Simulation Computationally Efficient Markov Chain Simulation Modal and Distributional Approximations REGRESSION MODELS Introduction to Regression Models Hierarchical Linear Models Generalized Linear Models Models for Robust Inference Models for Missing Data NONLINEAR AND NONPARAMETRIC MODELS Parametric Nonlinear Models Basic Function Models Gaussian Process Models Finite Mixture Models Dirichlet Process Models APPENDICES A: Standard Probability Distributions B: Outline of Proofs of Asymptotic Theorems C: Computation in R and Stan Bibliographic Notes and Exercises appear at the end of each chapter.