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Probability, Statistics, and Computational Science

  • Niko BeerenwinkelEmail author
  • Juliane Siebourg
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 855)

Abstract

In this chapter, we review basic concepts from probability theory and computational statistics that are fundamental to evolutionary genomics. We provide a very basic introduction to statistical modeling and discuss general principles, including maximum likelihood and Bayesian inference. Markov chains, hidden Markov models, and Bayesian network models are introduced in more detail as they occur frequently and in many variations in genomics applications. In particular, we discuss efficient inference algorithms and methods for learning these models from partially observed data. Several simple examples are given throughout the text, some of which point to models that are discussed in more detail in subsequent chapters.

Key words

Bayesian inference Bayesian networks Dynamic programming Expectation maximization algorithm Hidden Markov models Markov chains Maximum likelihood Statistical models 

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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Department of Biosystems Science and EngineeringETH ZurichBaselSwitzerland

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