| 書目名稱 | Likelihood, Bayesian, and MCMC Methods in Quantitative Genetics | | 編輯 | Daniel Sorensen,Daniel Gianola | | 視頻video | http://file.papertrans.cn/587/586127/586127.mp4 | | 概述 | Includes supplementary material: | | 叢書名稱 | Statistics for Biology and Health | | 圖書封面 |  | | 描述 | .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 quant | | 出版日期 | Book 2002 | | 關(guān)鍵詞 | Covariance matrix; Evolution; Excel; Multinomial distribution; Normal distribution; Poisson distribution; | | 版次 | 1 | | doi | https://doi.org/10.1007/b98952 | | isbn_softcover | 978-1-4419-2997-6 | | isbn_ebook | 978-0-387-22764-1Series ISSN 1431-8776 Series E-ISSN 2197-5671 | | issn_series | 1431-8776 | | copyright | Springer Science+Business Media New York 2002 |
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