Hidden Markov Models for Studying the Evolution of Binary Morphological Characters

摘要: Biologists now have the capability of building large phylogenetic trees consisting tens thousands species, from which important comparative questions can be addressed. However, to extent that biologists applied these data, it is clear current methods, such as those deal with evolution binary morphological characters, make unrealistic assumptions about how characters are modeled. As phylogenies increase both in size and scope, likely lability a character will differ significantly among lineages. In this chapter, we describe new generalized model, refer “hidden rates model” (HRM), used identify different discrete along branches phylogeny. The HRM part class models more broadly known Hidden Markov because presupposes unobserved “hidden” rate classes underlie each observed state represents potentially transition states. discuss, recognition accommodation heterogeneity provide robust picture evolution.