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Likelihood, Bayesian and MCMC Methods in Quantitative Genetics Book

Likelihood, Bayesian and MCMC Methods in Quantitative Genetics
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  • Likelihood, Bayesian and MCMC Methods in Quantitative Genetics
  • Written by author Daniel Sorensen
  • Published by Springer-Verlag New York, LLC, August 2002
  • Over the last ten years the introduction of computer intensive statistical methods has opened new horizons concerning the probability models that can be fitted to genetic data, the scale of the problems that can be tackled and the nature of the questions
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Authors

Preface
IReview of Probability and Distribution Theory1
1Probability and Random Variables3
2Functions of Random Variables77
IIMethods of Inference117
3An Introduction to Likelihood Inference119
4Further Topics in Likelihood Inference161
5An Introduction to Bayesian Inference211
6Bayesian Analysis of Linear Models287
7The Prior Distribution and Bayesian Analysis327
8Bayesian Assessment of Hypotheses and Models399
9Approximate Inference Via the EM Algorithm443
IIIMarkov Chain Monte Carlo Methods475
10An Overview of Discrete Markov Chains477
11Markov Chain Monte Carlo497
12Implementation and Analysis of MCMC Samples539
IVApplications in Quantitative Genetics561
13Gaussian and Thick-Tailed Linear Models563
14Threshold Models for Categorical Responses605
15Bayesian Analysis of Longitudinal Data627
16Segregation and Quantitative Trait Loci Analysis671
References701
List of Citations727
Subject Index733


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