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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 and the MCMC "revolution" has made a deep impact in quantitative genetics.
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, effort has been made to relate biological to statistical parameters throughout, and the book includes extensive examples that illustra
Content Level »Research
Keywords »Covariance matrix - Evolution - Excel - Multinomial distribution - Normal distribution - Poisson distribution - Probability distribution - Radiologieinformationssystem - Random variable - Variance - expectation–maximization algorithm - genes - genetics - linear regression - statistics