This book provides the foundations of likelihood, Bayesian and MCMC methods in the context of genetic analysis of quantitative traits. Effort has been made to relate biological to statistical parameters throughout, and extensive examples are included to illustrate the arguments.

This book, suitable for numerate biologists and for applied statisticians, provides the foundations of likelihood, Bayesian and MCMC methods in the context of genetic analysis of quantitative traits. Although a number of excellent texts in these areas have become available in recent years, the basic ideas and tools are typically described in a technically demanding style and contain much more detail than necessary. Here, effort has been made to relate biological to statistical parameters throughout, and the book includes extensive examples that illustrate the developing argument. Where most students in biology and agriculture lack the formal background needed to learn these modern biometrical techniques, this text bridges the gap by providing essential background material.

Includes supplementary material: sn.pub/extras

Autorentext

Daniel Sorensen holds PhD and DSc degrees from the University of Edinburgh and is an elected Fellow of the American Statistical Association. He was professor of Statistical Genetics at Aarhus University where, at present, he is professor emeritus.

Inhalt
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.