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Likelihood, Bayesian, and MCMC Methods in Quantitative Genetics

Daniel Sorensen, Daniel Gianola (Autoren)

Buch | Hardcover

740 Seiten

2002
Springer-Verlag New York Inc.
978-0-387-95440-0 (ISBN)

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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 application of Bayesian and likelihood methods to statistical genetics has been facilitated enormously by these methods. Techniques generally referred to as Markov chain Monte Carlo (MCMC) have played a major role in this process, stimulating synergies among scientists in different fields, such as mathematicians, probabilists, statisticians, computer scientists and statistical geneticists. Specifically, the MCMC "revolution" has made a deep impact in quantitative genetics. This can be seen, for example, in the vast number of papers dealing with complex hierarchical models and models for detection of genes affecting quantitative or meristic traits in plants, animals and humans that have been published recently.

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. Most students in biology and agriculture lack the formal background needed to learn these modern biometrical techniques. 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 have been written by and addressed to professional statisticians. For this reason, considerable more detail is offered than what may be warranted for a more mathematically apt audience.

The book is divided into four parts. Part I gives a review of probability and distribution theory. Parts II and III present methods of inference and MCMC methods. Part IV discusses several models that can be applied in quantitative genetics, primarily from a bayesian perspective.An effort has been made to relate biological to statistical parameters throughout, and examples are used profusely to motivate the developments.

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.

Reihe/Serie	Statistics for Biology and Health
Zusatzinfo	XVIII, 740 p.
Verlagsort	New York, NY
Sprache	englisch
Maße	156 x 234 mm
Themenwelt	Mathematik / Informatik ► Mathematik ► Angewandte Mathematik
	Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik
	Medizin / Pharmazie
	Naturwissenschaften ► Biologie ► Biochemie
	Naturwissenschaften ► Biologie ► Genetik / Molekularbiologie
ISBN-10	0-387-95440-6 / 0387954406
ISBN-13	978-0-387-95440-0 / 9780387954400
Zustand	Neuware