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Likelihood, Bayesian, and MCMC Methods in Quantitative Genetics

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Cover of 'Likelihood, Bayesian, and MCMC Methods in Quantitative Genetics'

Table of Contents

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    Book Overview
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    Chapter 1 Uncertainty, Random Variables, and Probability Distributions
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    Chapter 2 Uncertainty about Functions of Random Variables
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    Chapter 3 An Introduction to Likelihood Inference
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    Chapter 4 Further Topics in Likelihood Inference
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    Chapter 5 An Introduction to Bayesian Inference
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    Chapter 6 Bayesian Analysis of Linear Models
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    Chapter 7 The Prior Distribution and Bayesian Analysis
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    Chapter 8 Bayesian Assessment of Hypotheses and Models
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    Chapter 9 Approximate Inference Via the EM Algorithm
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    Chapter 10 An Overview of Discrete Markov Chains
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    Chapter 11 Markov Chain Monte Carlo
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    Chapter 12 Implementation and Analysis of MCMC Samples
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    Chapter 13 Gaussian and Thick-Tailed Linear Models
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    Chapter 14 Threshold Models for Categorical Responses
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    Chapter 15 Bayesian Analysis of Longitudinal Data
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    Chapter 16 Introduction to Segregation and Quantitative Trait Loci Analysis
Attention for Chapter 6: Bayesian Analysis of Linear Models
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Citations

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Chapter title
Bayesian Analysis of Linear Models
Chapter number 6
Book title
Likelihood, Bayesian, and MCMC Methods in Quantitative Genetics
Published by
Springer, New York, NY, January 2002
DOI 10.1007/0-387-22764-4_6
Book ISBNs
978-0-387-95440-0, 978-0-387-22764-1
Authors

Daniel Sorensen, Daniel Gianola

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 1 Mendeley reader of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 100%
Readers by discipline Count As %
Agricultural and Biological Sciences 1 100%

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