LEADER 03598oam 2200649I 450 001 9910787845903321 005 20230803195655.0 010 $a0-429-18426-3 010 $a1-4665-0079-4 024 7 $a10.1201/b16965 035 $a(CKB)2670000000557118 035 $a(EBL)1480380 035 $a(SSID)ssj0001218169 035 $a(PQKBManifestationID)11707656 035 $a(PQKBTitleCode)TC0001218169 035 $a(PQKBWorkID)11211310 035 $a(PQKB)10882540 035 $a(OCoLC)880825218 035 $a(MiAaPQ)EBC1480380 035 $a(EXLCZ)992670000000557118 100 $a20180331h20142014 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aBayesian phylogenetics $emethods, algorithms, and applications /$fedited by Ming-Hui Chen, Lynn Kuo, and Paul O. Lewis, University of Connecticut Storrs, USA 210 1$aBoca Raton :$cCRC Press,$d[2014] 210 4$dİ2014 215 $a1 online resource (391 p.) 225 1 $aChapman and Hall/CRC Mathematical and Computational Biology Series 300 $aDescription based upon print version of record. 311 $a1-306-86745-2 311 $a1-4665-0082-4 320 $aIncludes bibliographical references. 327 $aFront Cover; Contents; List of Figures; List of Tables; Preface; Editors; Contributors; Chapter 1: Bayesian phylogenetics: methods, computational algorithms, and applications; Chapter 2: Priors in Bayesian phylogenetics; Chapter 3: Inated density ratio (IDR) method for estimating marginal likelihoods in Bayesian phylogenetics; Chapter 4: Bayesian model selection in phylogenetics and genealogy-based population genetics; Chapter 5: Variable tree topology stepping-stone marginal likelihood estimation; Chapter 6: Consistency of marginal likelihood estimation when topology varies 327 $aChapter 7: Bayesian phylogeny analysisChapter 8: SMC (sequential Monte Carlo) for Bayesian phylogenetics; Chapter 9: Population model comparison using multi-locus datasets; Chapter 10: Bayesian methods in the presence of recombination; Chapter 11: Bayesian nonparametric phylodynamics; Chapter 12: Sampling and summary statistics of endpoint-conditioned paths in DNA sequence evolution; Chapter 13: Bayesian inference of species divergence times; Bibliography; Back Cover 330 $aOffering a rich diversity of models, Bayesian phylogenetics allows evolutionary biologists, systematists, ecologists, and epidemiologists to obtain answers to very detailed phylogenetic questions. Suitable for graduate-level researchers in statistics and biology, Bayesian Phylogenetics: Methods, Algorithms, and Applications presents a snapshot of current trends in Bayesian phylogenetic research. Encouraging interdisciplinary research, this book introduces state-of-the-art phylogenetics to the Bayesian statistical community and, likewise, presents state-of-the- 410 0$aChapman and Hall/CRC mathematical & computational biology series. 606 $aPhylogeny 606 $aBiometry 606 $aMolecular genetics 606 $aBayesian statistical decision theory 615 0$aPhylogeny. 615 0$aBiometry. 615 0$aMolecular genetics. 615 0$aBayesian statistical decision theory. 676 $a576.8/8 702 $aChen$b Ming-Hui$f1961- 702 $aKuo$b Lynn$f1949- 702 $aLewis$b Paul O.$f1961- 801 0$bFlBoTFG 801 1$bFlBoTFG 906 $aBOOK 912 $a9910787845903321 996 $aBayesian phylogenetics$93806851 997 $aUNINA