Incorporating compositional heterogeneity into Lie Markov models for phylogenetic inference
نویسندگان
چکیده
منابع مشابه
Lie Markov models.
Recent work has discussed the importance of multiplicative closure for the Markov models used in phylogenetics. For continuous-time Markov chains, a sufficient condition for multiplicative closure of a model class is ensured by demanding that the set of rate-matrices belonging to the model class form a Lie algebra. It is the case that some well-known Markov models do form Lie algebras and we re...
متن کاملIncorporating motivational heterogeneity into game-theoretic models of collective action
Understanding cooperation in the context of social dilemma games is fundamental to understanding how alternative institutional arrangements may foster collective action in such settings. An abundance of experimental evidence is inconsistent with predictions from game-theoretic models based strictly on self-regarding utilities. In recent years, scholars have turned to alternative representations...
متن کاملPhylogenetic Hidden Markov Models
Phylogenetic hidden Markov models, or phylo-HMMs, are probabilistic models that consider not only the way substitutions occur through evolutionary history at each site of a genome, but also the way this process changes from one site to the next. By treating molecular evolution as a combination of two Markov processes—one that operates in the dimension of space (along a genome) and one that oper...
متن کاملAccounting for gene rate heterogeneity in phylogenetic inference.
Traditionally, phylogenetic analyses over many genes combine data into a contiguous block. Under this concatenated model, all genes are assumed to evolve at the same rate. However, it is clear that genes evolve at very different rates and that accounting for this rate heterogeneity is important if we are to accurately infer phylogenies from heterogeneous multigene data sets. There remain open q...
متن کاملStochastic variational inference for hidden Markov models
Variational inference algorithms have proven successful for Bayesian analysis in large data settings, with recent advances using stochastic variational inference (SVI). However, such methods have largely been studied in independent or exchangeable data settings. We develop an SVI algorithm to learn the parameters of hidden Markov models (HMMs) in a time-dependent data setting. The challenge in ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: The Annals of Applied Statistics
سال: 2020
ISSN: 1932-6157
DOI: 10.1214/20-aoas1369