Author: Daniel Sorensen
Edition:
Publisher: Springer
Binding: Hardcover
ISBN: 0387954406
Likelihood, Bayesian and MCMC Methods in Quantitative Genetics (Statistics for Biology and Health)
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. Medical books Likelihood, Bayesian and MCMC Methods in Quantitative Genetics . 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 Medical books Likelihood Bayesian And Mcmc Methods In Quantitative Genetics. Springer. Hardcover. 0387954406 Hardcover; 2007 Springer; 760 pages; "Likelihood Bayesian and MCMC Methods in Quantitative Genetics" by Daniel Sorensen et al. . Very Good.
Download link for Likelihood, Bayesian and MCMC Methods in Quantitative Genetics
Springer. Hardcover. 0387954406 Hardcover; 2007 Springer; 760 pages; "Likelihood Bayesian and MCMC Methods in Quantitative Genetics" by Daniel Sorensen et al. . Very Good.
Likelihood, Bayesian And Mcmc Methods in Quantitative Genetics
Likelihood, Bayesian and Mcmc Methods in Quantitative Genetics SPRIV 9781441929976 09781441929976
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, an effort has been made to relate biological to statistical parameters throughout, and the book includes extensive examples that illustrate the developing argument.
Medical Book Likelihood, Bayesian and MCMC Methods in Quantitative Genetics
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, an effort has been made to relate biological to statistical parameters throughout, and the book includes extensive examples that illustrate the developing argument.
