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Journal article

Probability, Statistics, and Computational Science.

  • Beerenwinkel N Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland. niko.beerenwinkel@bsse.ethz.ch.
  • Siebourg J Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.
  • 2019-07-07
Published in:
  • Methods in molecular biology (Clifton, N.J.). - 2019
Bayesian inference
Bayesian networks
Dynamic programming
EM algorithm
Hidden Markov models
Markov chains
Maximum likelihood
Statistical models
Algorithms
Bayes Theorem
Computational Biology
Humans
Likelihood Functions
Markov Chains
Models, Statistical
Probability
English 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 provide the basis for models that are discussed in more detail in subsequent chapters.
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  • English
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hybrid
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https://sonar.ch/global/documents/98790
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