作者: Nobuyuki Kutsukake , Hideki Innan
DOI: 10.1007/978-3-662-43550-2_17
关键词: Value (computer science) 、 Phylogenetic comparative methods 、 Pattern recognition 、 Brownian motion 、 Selection (genetic algorithm) 、 Computer science 、 Artificial intelligence 、 Directional selection 、 Trait 、 Algorithm 、 Approximate Bayesian computation 、 Likelihood 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.