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Mathematical and Statistical Methods for Genetic Analysis pp 142–163Cite as

Markov Chain Monte Carlo Methods

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Part of the book series: Statistics for Biology and Health ((SBH))

Abstract

Mapping disease and marker loci from pedigree phenotypes is one of the most computationally onerous tasks in modern biology. Even tightly optimized software can be quickly overwhelmed by the synergistic obstructions of missing data, multiple marker loci, multiple alleles per marker locus, and inbreeding. This unhappy situation has prompted mathematical and statistical geneticists to adapt recent stochastic methods for numerical integration to the demands of pedigree analysis [9, 15, 16, 21, 25, 26, 28]. The current chapter explains in a concrete genetic setting how these powerful stochastic methods operate.

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Authors and Affiliations

  1. Department of Biostatistics and Mathematics, University of Michigan, 48109-2029, Ann Arbor, MI, USA

    Kenneth Lange

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  1. Kenneth Lange
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© 1997 Springer Science+Business Media New York

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Lange, K. (1997). Markov Chain Monte Carlo Methods. In: Mathematical and Statistical Methods for Genetic Analysis. Statistics for Biology and Health. Springer, New York, NY. https://doi.org/10.1007/978-1-4757-2739-5_9

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  • DOI: https://doi.org/10.1007/978-1-4757-2739-5_9

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4757-2741-8

  • Online ISBN: 978-1-4757-2739-5

  • eBook Packages: Springer Book Archive

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