Chapter: Bayesian Analysis of Linear Models

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Home > Likelihood, Bayesian, and MCMC Methods in Quantitative Genetics > Bayesian Analysis of Linear Models

Likelihood, Bayesian, and MCMC Methods in Quantitative Genetics |

Daniel Sorensen; Daniel Gianola

Springer New York

Likelihood, Bayesian, and MCMC Methods in Quantitative Genetics

Bayesian Analysis of Linear Models

Chapter Info

Authors

Daniel Sorensen; Daniel Gianola

ARI Id

1664077802365_226797

Access

Not Available Free

Pages

287-326

DOI

10.1007/0-387-22764-4_6

Chapter URL

https://rd.springer.com/chapter/10.1007/0-387-22764-4_6

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Chapters/Headings	Author(s)	Pages	Info
Front Matter		i-xvii
Front Matter		1-1
Uncertainty, Random Variables, and Probability Distributions	Daniel Sorensen; Daniel Gianola	3-75
Uncertainty about Functions of Random Variables	Daniel Sorensen; Daniel Gianola	77-116
Front Matter		117-117
An Introduction to Likelihood Inference	Daniel Sorensen; Daniel Gianola	119-160
Further Topics in Likelihood Inference	Daniel Sorensen; Daniel Gianola	161-209
An Introduction to Bayesian Inference	Daniel Sorensen; Daniel Gianola	211-285
Bayesian Analysis of Linear Models	Daniel Sorensen; Daniel Gianola	287-326
The Prior Distribution and Bayesian Analysis	Daniel Sorensen; Daniel Gianola	327-397
Bayesian Assessment of Hypotheses and Models	Daniel Sorensen; Daniel Gianola	399-442
Approximate Inference Via the EM Algorithm	Daniel Sorensen; Daniel Gianola	443-473
Front Matter		475-475
An Overview of Discrete Markov Chains	Daniel Sorensen; Daniel Gianola	477-496
Markov Chain Monte Carlo	Daniel Sorensen; Daniel Gianola	497-537
Implementation and Analysis of MCMC Samples	Daniel Sorensen; Daniel Gianola	539-560
Front Matter		561-561
Gaussian and Thick-Tailed Linear Models	Daniel Sorensen; Daniel Gianola	563-604
Threshold Models for Categorical Responses	Daniel Sorensen; Daniel Gianola	605-626
Bayesian Analysis of Longitudinal Data	Daniel Sorensen; Daniel Gianola	627-670
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