Phylogenetic estimation with partial likelihood tensors

Maximum Likelihood Methods for Phylogenetic Inference

In this article, we provide an overview of maximum likelihood methods for phylogenetic inference. A brief introduction to general maximum likelihood estimation is provided. We define a phylogenetic likelihood, summarize how to compute this likelihood, and then discuss approaches used to maximize the phylogenetic likelihood function. We discuss a property of the maximum likelihood estimation, ca...

فیلوژنی مولکولی جنس Eumeces Wiegmann, 1834 (خزندگان: سینسیده) در ایران، براساس DNA میتوکندریایی ژن 16S

Phylogenetic relationships among the Eumeces schneiderii princeps and Eumeces schneiderii pavimentatus investigated using 509 bp partial sequences of 16S mitochondrial gene. Analyses were done by maximum-likelihood (RAxML) criteria on 52 specimens from over 20 geographically distinct localities. Our molecular results proposed two well-supported major clades by their phylogenetic positions, gene...

Maximum Likelihood Estimation and Em Fixed Point Ideals for Binary Tensors

Study on phylogenetic status of Hari barbel Luciobarbus conocephalus (Kessler, 1872) from Hari river using Cytb gene

Recently, Luciobarbus conocephalus from the Hari River was reported for the first time, but there is doubt about the validity of this species between authors, because some of them placed it as a subspecies or synonym of L. capito. Therefore, the present study was conducted to investigate the status of phylogeny and the validity of this species. For this purpose, specimens captured from Hari Riv...

Quantum Simulation of Phylogenetic Trees

Quantum simulations constructing probability tensors of biological multi-taxa in phylogenetic trees are proposed, in terms of positive trace preserving maps, describing evolving systems of quantum walks with multiple walkers. Basic phylogenetic models applying on trees of various topologies are simulated following appropriate decoherent quantum circuits. Quantum simulations of statistical infer...