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Science/Engineering Statistical Mechanics, Third Edition
Science/Engineering Essentials of Toxic Chemical Risk: Science and Society
Science/Engineering Telefoncoaching: So machen Sie aus Ihren Mitarbeitern Telefonprofis
Science/Engineering Wireless Communications (Wiley - IEEE)
Science/Engineering Posttraumatische Belastungsstörungen (German Edition)
Science/Engineering Lernplattformen in Schulen: Ansätze für E-Learning und Blended Learning in Präsenzklassen (1 Auflage)
Science/Engineering Stochastik für Einsteiger: Eine Einführung in die faszinierende Welt des Zufalls. Mit über 220 Übungsaufgaben und Lösungen {Repost}
Science/Engineering Testtheorie und Fragebogenkonstruktion (Springer-Lehrbuch)
Science/Engineering Centrifugal Pumps, 2nd Edition
Science/Engineering Computational Intelligence for Modelling and Prediction (Studies in Computational Intelligence) 1 edition {Repost}
Science/Engineering Networks, Crowds, and Markets: Reasoning About a Highly Connected World {repost}
Science/Engineering Introduction to Biophotonics (repost)
Science/Engineering The Art and Science of Psychotherapy (repost)
Science/Engineering Advances in Chemical Physics - Volume 15: Stochastic Processes in Chemical Physics
Science/Engineering "Emulsion Science: Basic Principles" (repost)
Science/Engineering Elementary Principles of Chemical Processes 3rd edition
Science/Engineering Boundary Element Analysis (repost)
Science/Engineering Collection of books on physics 2
Science/Engineering A Practical Handbook of Preparative HPLC by Donald A. Wellings (Repost)
Science/Engineering Reviews of Environmental Contamination and Toxicology 184 by George W. Ware
Science/Engineering Essentials of Toxic Chemical Risk: Science and Society
Science/Engineering Telefoncoaching: So machen Sie aus Ihren Mitarbeitern Telefonprofis
Science/Engineering Wireless Communications (Wiley - IEEE)
Science/Engineering Posttraumatische Belastungsstörungen (German Edition)
Science/Engineering Lernplattformen in Schulen: Ansätze für E-Learning und Blended Learning in Präsenzklassen (1 Auflage)
Science/Engineering Stochastik für Einsteiger: Eine Einführung in die faszinierende Welt des Zufalls. Mit über 220 Übungsaufgaben und Lösungen {Repost}
Science/Engineering Testtheorie und Fragebogenkonstruktion (Springer-Lehrbuch)
Science/Engineering Centrifugal Pumps, 2nd Edition
Science/Engineering Computational Intelligence for Modelling and Prediction (Studies in Computational Intelligence) 1 edition {Repost}
Science/Engineering Networks, Crowds, and Markets: Reasoning About a Highly Connected World {repost}
Science/Engineering Introduction to Biophotonics (repost)
Science/Engineering The Art and Science of Psychotherapy (repost)
Science/Engineering Advances in Chemical Physics - Volume 15: Stochastic Processes in Chemical Physics
Science/Engineering "Emulsion Science: Basic Principles" (repost)
Science/Engineering Elementary Principles of Chemical Processes 3rd edition
Science/Engineering Boundary Element Analysis (repost)
Science/Engineering Collection of books on physics 2
Science/Engineering A Practical Handbook of Preparative HPLC by Donald A. Wellings (Repost)
Science/Engineering Reviews of Environmental Contamination and Toxicology 184 by George W. Ware
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Science/Engineering Likelihood, Bayesian and MCMC Methods in Quantitative Genetics
Posted on 2010-03-15
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More Likelihood, Bayesian and MCMC Methods in Quantitative Genetics 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. Daniel Sorensen is a Research Professor in Statistical Genetics, at the Department of Animal Breeding and Genetics in the Danish Institute of Agricultural Sciences. Daniel Gianola is Professor in the Animal Sciences, Biostatistics and Medical Informatics, and Dairy Science Departments of the University of Wisconsin-Madison. Gianola and Sorensen pioneered the introduction of Bayesian and MCMC methods in animal breeding. The authors have published and lectured extensively in applications of statistics to quantitative genetics. Book Info 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.
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