Likelihood, Bayesian, and MCMC Methods in Quantitative Genetics
Likelihood, Bayesian, and MCMC Methods in Quantitative Genetics (Statistics for Biology and Health)
- 作者: Daniel Sorensen,Daniel Gianola
- 出版社/メーカー: Springer
- 発売日: 2002/08/12
- メディア: ハードカバー
- クリック: 12回
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- 厚めの本だけれど、ベイズ、事前・事後確率、それを使うための基礎とそれを使ったもろもろについて参照しやすそう
- I Review of Probability and Distribution Theory
- 1 Probability and Random Variables
- 2 Functions of Random Variables
- II Methods of Inference
- 3 An Introduction to Likelihood Inference
- 4 Further Topics in Likelihood Inference
- 5 An Introduction to Bayesian Inference
- 6 Bayesian Analysis of Linear Models
- 7 The Prior Distribution and Bayesian Analysis
- 8 Bayesian Assessment of Hypotheses and Models
- 9 Approximate Inference Via the EM Algorithm
- III Markov Chain Monte Carlo Methods
- 10 An Overview of Discrete Markov Chains
- 11 Markov Chain Monte Carlo
- 12 Implementation and Analysis of MCMC Samples
- IV Applications in Quantitative Genetics
- 13 Gaussian and Thick-Tailed Linear Models
- 14 Threshold Models for Categorical Responses
- 15 Bayesian Analysis of Longitudinal Data
- 16 Segregation and Quantitative Trait Loci Analysis
