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Since their popularization in the 1990s, Markov chain Monte Carlo (MCMC) methods have revolutionized statistical computing and have had an especially profound impact on the practice of Bayesian statistics. Furthermore, MCMC methods have enabled the development and use of intricate models in an astonishing array of disciplines as diverse as fisherie
In a family study of breast cancer, epidemiologists in Southern California increase the power for detecting a gene-environment interaction. In Gambia, a study helps a vaccination program reduce the incidence of Hepatitis B carriage. Archaeologists in Austria place a Bronze Age site in its true temporal location on the calendar scale. And in France,
Markov Chain Monte Carlo (MCMC) originated in statistical physics, but has spilled over into various application areas, leading to a corresponding variety of techniques and methods. That variety stimulates new ideas and developments from many different places, and there is much to be gained from cross-fertilization. This book presents five expository essays by leaders in the field, drawing from perspectives in physics, statistics and genetics, and showing how different aspects of MCMC come to the fore in different contexts. The essays derive from tutorial lectures at an interdisciplinary program at the Institute for Mathematical Sciences, Singapore, which exploited the exciting ways in which MCMC spreads across different disciplines.
This handbook offers guidance on selections of appropriate computational methods and software packages for specific genetic problems. Coverage strikes a balance between methodological expositions and practical guidelines for software selections. Wherever possible, comparisons among competing methods and software are made to highlight the relative advantages and disadvantage of the approaches.
Annotation While this monograph is not about show dogs or cats, its statistical methods could be applied to tracing the pedigree of these species as well as humans. Thompson (U. of Washington) covers such topics as genetic models, population allele frequencies, kinship/inbreeding coefficients, and Monte Carlo estimation. Includes supporting tables and figures. Suitable as a supplementary text or primary text for advanced students. Lacks an index. c. Book News Inc.
Genetics mapping, physical mapping and DNA sequencing are the three key components of the human and other genome projects. Statistics, mathematics and computing play important roles in all three, as well as in the uses to which the mapping and sequencing data are put. This volume edited by key researchers Mike Waterman and Terry Speed reviews recent progress in the area, with an emphasis on the theory and application of genetic mapping.
Geneticists now stand on the threshold of sequencing the genome in its entirety. The unprecedented insights into human disease and evolution offered by mapping and sequencing are transforming medicine and agriculture. This revolution depends vitally on the contributions made by applied mathematicians, statisticians, and computer scientists. Kenneth Lange has written a book to enable graduate students in the mathematical sciences to understand and model the epidemiological and experimental data encountered in genetics research. Mathematical, statistical, and computational principles relevant to this task are developed hand-in-hand with applications to gene mapping, risk prediction, and the testing of epidemiological hypotheses. The book covers many topics previously only accessible in journal articles, such as pedigree analysis algorithms, Markov chain, Monte Carlo methods, reconstruction of evolutionary trees, radiation hybrid mapping, and models of recombination. The whole is backed by numerous exercise sets.
Analysis of Genetic Association Studies is both a graduate level textbook in statistical genetics and genetic epidemiology, and a reference book for the analysis of genetic association studies. Students, researchers, and professionals will find the topics introduced in Analysis of Genetic Association Studies particularly relevant. The book is applicable to the study of statistics, biostatistics, genetics and genetic epidemiology. In addition to providing derivations, the book uses real examples and simulations to illustrate step-by-step applications. Introductory chapters on probability and genetic epidemiology terminology provide the reader with necessary background knowledge. The organization of this work allows for both casual reference and close study.
From the reviews: The purpose of the book under review is to give a survey of methods for the Bayesian or likelihood-based analysis of data. The author distinguishes between two types of methods: the observed data methods and the data augmentation ones. The observed data methods are applied directly to the likelihood or posterior density of the observed data. The data augmentation methods make use of the special "missing" data structure of the problem. They rely on an augmentation of the data which simplifies the likelihood or posterior density. #Zentralblatt für Mathematik#