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Springer Likelihood, Bayesian, and MCMC Methods in Genetics

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Description

Explore the evolution of statistical genetics with this Springer publication. Over the last decade, computer intensive statistical methods have expanded the possibilities for fitting probability models to genetic data. This text covers the significant impact of Bayesian and likelihood methods on the field of statistical genetics. By focusing on Markov chain Monte Carlo (MCMC) techniques, this book examines how these computational advancements have allowed researchers to tackle larger scales of problems and pose new types of scientific questions. It serves as a resource for understanding the synergies created between different scientific fields through these advanced mathematical approaches.

Key Features

This Springer book covers advanced likelihood and Bayesian methods applied to statistical genetics.

Learn how Markov chain Monte Carlo (MCMC) techniques have changed the landscape of genetic data analysis.

Explore how computer intensive statistical methods allow for more complex probability models in biology.

Understand the scale of modern problems and the new types of questions possible in quantitative genetics.

This text examines the synergies between different scientific fields driven by advanced mathematical methods.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
12 August 2002
Listed Since
12 February 2007

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No barcode data available

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