Likelihood, Bayesian, and MCMC Methods in Quantitative Genetics

Likelihood, Bayesian, and MCMC Methods in Quantitative Genetics by Daniel Sorensen, Daniel Gianola.

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 that can be posed. In particular, the applica...

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Bibliographic Details
Main Authors: Sorensen, Daniel (Author), Gianola, Daniel (Author)
Corporate Author: SpringerLink (Online service)
Format: eBook
Language:English
Published: New York, NY : Springer New York : Imprint: Springer, 2002.
Edition:1st ed. 2002.
Series:Statistics for Biology and Health,
Springer eBook Collection.
Subjects:
Biochemistry.
Statistics .
Animal genetics.
Plant genetics.
Electronic resources (E-books)
Online Access:Click to view e-book
Holy Cross Note:Loaded electronically.
Electronic access restricted to members of the Holy Cross Community.
Table of Contents:
  • Review of Probability and Distribution Theory
  • Uncertainty, Random Variables, and Probability Distributions
  • Uncertainty about Functions of Random Variables
  • Methods of Inference
  • An Introduction to Likelihood Inference
  • Further Topics in Likelihood Inference
  • An Introduction to Bayesian Inference
  • Bayesian Analysis of Linear Models
  • The Prior Distribution and Bayesian Analysis
  • Bayesian Assessment of Hypotheses and Models
  • Approximate Inference Via the EM Algorithm
  • Markov Chain Monte Carlo Methods
  • An Overview of Discrete Markov Chains
  • Markov Chain Monte Carlo
  • Implementation and Analysis of MCMC Samples
  • Applications in Quantitative Genetics
  • Gaussian and Thick-Tailed Linear Models
  • Threshold Models for Categorical Responses
  • Bayesian Analysis of Longitudinal Data
  • to Segregation and Quantitative Trait Loci Analysis.

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