Matches in SemOpenAlex for { <https://semopenalex.org/work/W2064291430> ?p ?o ?g. }
- W2064291430 endingPage "726" @default.
- W2064291430 startingPage "711" @default.
- W2064291430 abstract "In recent years, the advent of Markov chain Monte Carlo (MCMC) techniques, coupled with modern computational capabilities, has enabled the study of evolutionary models without a closed form solution of the likelihood function. However, current Bayesian MCMC applications can incur significant computational costs, as they are based on a full sampling from the posterior probability distribution of the parameters of interest. Here, we draw attention as to how MCMC techniques can be embedded within normal approximation strategies for more economical statistical computation. The overall procedure is based on an estimate of the first and second moments of the likelihood function, as well as a maximum likelihood estimate. Through examples, we review several MCMC-based methods used in the statistical literature for such estimation, applying the approaches to constructing posterior distributions under non-analytical evolutionary models relaxing the assumptions of rate homogeneity, and of independence between sites. Finally, we use the procedures for conducting Bayesian model selection, based on Laplace approximations of Bayes factors, which we find to be accurate and computationally advantageous. Altogether, the methods we expound here, as well as other related approaches from the statistical literature, should prove useful when investigating increasingly complex descriptions of molecular evolution, alleviating some of the difficulties associated with nonanalytical models." @default.
- W2064291430 created "2016-06-24" @default.
- W2064291430 creator A5014774866 @default.
- W2064291430 creator A5020417939 @default.
- W2064291430 creator A5057909697 @default.
- W2064291430 date "2007-10-01" @default.
- W2064291430 modified "2023-10-17" @default.
- W2064291430 title "Exploring Fast Computational Strategies for Probabilistic Phylogenetic Analysis" @default.
- W2064291430 cites W1589812083 @default.
- W2064291430 cites W1950772819 @default.
- W2064291430 cites W1964735403 @default.
- W2064291430 cites W1975017420 @default.
- W2064291430 cites W1975041634 @default.
- W2064291430 cites W1977862457 @default.
- W2064291430 cites W1983408155 @default.
- W2064291430 cites W1986342384 @default.
- W2064291430 cites W1996356638 @default.
- W2064291430 cites W1996778526 @default.
- W2064291430 cites W2003471362 @default.
- W2064291430 cites W2008668767 @default.
- W2064291430 cites W2017696952 @default.
- W2064291430 cites W2024060531 @default.
- W2064291430 cites W2025183033 @default.
- W2064291430 cites W2033496784 @default.
- W2064291430 cites W2036272181 @default.
- W2064291430 cites W2036375318 @default.
- W2064291430 cites W2039312794 @default.
- W2064291430 cites W2039625353 @default.
- W2064291430 cites W2041802555 @default.
- W2064291430 cites W2045091067 @default.
- W2064291430 cites W2047834101 @default.
- W2064291430 cites W2049964725 @default.
- W2064291430 cites W2052576140 @default.
- W2064291430 cites W2053929098 @default.
- W2064291430 cites W2056760934 @default.
- W2064291430 cites W2061140759 @default.
- W2064291430 cites W2062018285 @default.
- W2064291430 cites W2070112803 @default.
- W2064291430 cites W2072843928 @default.
- W2064291430 cites W2074282020 @default.
- W2064291430 cites W2090692107 @default.
- W2064291430 cites W2091276705 @default.
- W2064291430 cites W2095727666 @default.
- W2064291430 cites W2096619551 @default.
- W2064291430 cites W2098538101 @default.
- W2064291430 cites W2098601118 @default.
- W2064291430 cites W2099364951 @default.
- W2064291430 cites W2100381682 @default.
- W2064291430 cites W2101546326 @default.
- W2064291430 cites W2102424972 @default.
- W2064291430 cites W2102862543 @default.
- W2064291430 cites W2103546861 @default.
- W2064291430 cites W2121061814 @default.
- W2064291430 cites W2121168074 @default.
- W2064291430 cites W2124790653 @default.
- W2064291430 cites W2131527832 @default.
- W2064291430 cites W2137188869 @default.
- W2064291430 cites W2138309709 @default.
- W2064291430 cites W2142635246 @default.
- W2064291430 cites W2144083783 @default.
- W2064291430 cites W2145937369 @default.
- W2064291430 cites W2147307434 @default.
- W2064291430 cites W2152977846 @default.
- W2064291430 cites W2154284350 @default.
- W2064291430 cites W2159748368 @default.
- W2064291430 cites W2160364329 @default.
- W2064291430 cites W2169079604 @default.
- W2064291430 cites W2169882791 @default.
- W2064291430 cites W4210747366 @default.
- W2064291430 cites W4211177544 @default.
- W2064291430 cites W4230357098 @default.
- W2064291430 cites W4231070980 @default.
- W2064291430 cites W4245883374 @default.
- W2064291430 cites W4292691288 @default.
- W2064291430 cites W4299551239 @default.
- W2064291430 doi "https://doi.org/10.1080/10635150701611258" @default.
- W2064291430 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/17849326" @default.
- W2064291430 hasPublicationYear "2007" @default.
- W2064291430 type Work @default.
- W2064291430 sameAs 2064291430 @default.
- W2064291430 citedByCount "14" @default.
- W2064291430 countsByYear W20642914302012 @default.
- W2064291430 countsByYear W20642914302013 @default.
- W2064291430 countsByYear W20642914302014 @default.
- W2064291430 countsByYear W20642914302015 @default.
- W2064291430 countsByYear W20642914302019 @default.
- W2064291430 countsByYear W20642914302020 @default.
- W2064291430 countsByYear W20642914302021 @default.
- W2064291430 crossrefType "journal-article" @default.
- W2064291430 hasAuthorship W2064291430A5014774866 @default.
- W2064291430 hasAuthorship W2064291430A5020417939 @default.
- W2064291430 hasAuthorship W2064291430A5057909697 @default.
- W2064291430 hasBestOaLocation W20642914301 @default.
- W2064291430 hasConcept C107673813 @default.
- W2064291430 hasConcept C111350023 @default.
- W2064291430 hasConcept C11413529 @default.
- W2064291430 hasConcept C119857082 @default.
- W2064291430 hasConcept C142291917 @default.