Beerenwinkel N Siebourg J 2019 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. application/pdf https://sonar.ch/global/documents/98790 eng info:eu-repo/semantics/altIdentifier/doi/10.1007/978-1-4939-9074-0_2 info:eu-repo/semantics/altIdentifier/pmid/31278661 info:eu-repo/semantics/openAccess 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 Probability, Statistics, and Computational Science. http://purl.org/coar/resource_type/c_6501