Detecting Phenotypic Selection by Approximate Bayesian Computation in Phylogenetic Comparative Methods

作者: Nobuyuki Kutsukake , Hideki Innan

DOI: 10.1007/978-3-662-43550-2_17

关键词: Value (computer science)Phylogenetic comparative methodsPattern recognitionBrownian motionSelection (genetic algorithm)Computer scienceArtificial intelligenceDirectional selectionTraitAlgorithmApproximate Bayesian computationLikelihood function

摘要: This chapter discusses the fundamental structure and advantages of approximate Bayesian computation (ABC) algorithm in phylogenetic comparative methods (PCMs). ABC estimates unknown parameters as follows: (1) simulated data are generated under a suite randomly chosen from their prior distributions; (2) compared with empirical data; (3) accepted when distance between is small; (4) by repeating steps (1)–(3), posterior distributions will be gained. Because does not necessitate mathematical expression or analytic solution likelihood function, particularly useful maximum-likelihood (ML) estimation difficult to conduct (a common situation testing complex evolutionary models and/or many PCMs). As an application, we analysed trait evolution which specific species exhibits extraordinary value relative others. The approach detected occurrence branch-specific directional selection estimated ancestral states internal nodes. computational power increases, such likelihood-free approaches become increasingly for PCMs, that deviate standard based on Brownian motion.

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