@ARTICLE{TreeBASE2Ref16558,
author = {David C. Marshall and Chris Simon and Thomas R. Buckley},
title = {Accurate Branch Length Estimation in Partitioned Bayesian Analyses Requires Accommodation of Among-Partition Rate Variation and Attention to Branch Length Priors.},
year = {2006},
keywords = {},
doi = {10.1080/10635150601087641},
url = {},
pmid = {},
journal = {Systematic Biology},
volume = {55},
number = {6},
pages = {993--1003},
abstract = {Molecular phylogenetic studies are making increasing use of partitioned Bayesian analyses via software tools like MrBayes version 3. Data partitioning is important because, as long as the same topology/history underlies all of the partitions, it addresses some of the problems associated with the combination of data sets with heterogeneous rates and eliminates the need to argue the validity of tests that have been used to judge data combinability. In addition, new studies indicate that data partitioning and the use of mixed models often dramatically improve the fit of model to data without the cost of overparameterization. While applying partitioned models to studies of protein-coding mitochondrial data, we have found that analyses using MrBayes may infer overly ?long? trees if among-partition rate variation (APRV) is not explicitly accommodated and if data from different partitions evolve at different average rates.}
}
Citation for Study 1679
Citation title:
"Accurate Branch Length Estimation in Partitioned Bayesian Analyses Requires Accommodation of Among-Partition Rate Variation and Attention to Branch Length Priors.".
This study was previously identified under the legacy study ID S1641
(Status: Published).
Citation
Marshall D., Simon C., & Buckley T. 2006. Accurate Branch Length Estimation in Partitioned Bayesian Analyses Requires Accommodation of Among-Partition Rate Variation and Attention to Branch Length Priors. Systematic Biology, 55(6): 993-1003.
Authors
-
Marshall D.
-
Simon C.
-
Buckley T.
Abstract
Molecular phylogenetic studies are making increasing use of partitioned Bayesian analyses via software tools like MrBayes version 3. Data partitioning is important because, as long as the same topology/history underlies all of the partitions, it addresses some of the problems associated with the combination of data sets with heterogeneous rates and eliminates the need to argue the validity of tests that have been used to judge data combinability. In addition, new studies indicate that data partitioning and the use of mixed models often dramatically improve the fit of model to data without the cost of overparameterization. While applying partitioned models to studies of protein-coding mitochondrial data, we have found that analyses using MrBayes may infer overly ?long? trees if among-partition rate variation (APRV) is not explicitly accommodated and if data from different partitions evolve at different average rates.
External links
About this resource
- Canonical resource URI:
http://purl.org/phylo/treebase/phylows/study/TB2:S1679
- Other versions:
Nexus
NeXML
- Show BibTeX reference
@ARTICLE{TreeBASE2Ref16558,
author = {David C. Marshall and Chris Simon and Thomas R. Buckley},
title = {Accurate Branch Length Estimation in Partitioned Bayesian Analyses Requires Accommodation of Among-Partition Rate Variation and Attention to Branch Length Priors.},
year = {2006},
keywords = {},
doi = {10.1080/10635150601087641},
url = {},
pmid = {},
journal = {Systematic Biology},
volume = {55},
number = {6},
pages = {993--1003},
abstract = {Molecular phylogenetic studies are making increasing use of partitioned Bayesian analyses via software tools like MrBayes version 3. Data partitioning is important because, as long as the same topology/history underlies all of the partitions, it addresses some of the problems associated with the combination of data sets with heterogeneous rates and eliminates the need to argue the validity of tests that have been used to judge data combinability. In addition, new studies indicate that data partitioning and the use of mixed models often dramatically improve the fit of model to data without the cost of overparameterization. While applying partitioned models to studies of protein-coding mitochondrial data, we have found that analyses using MrBayes may infer overly ?long? trees if among-partition rate variation (APRV) is not explicitly accommodated and if data from different partitions evolve at different average rates.}
}
- Show RIS reference
TY - JOUR
ID - 16558
AU - Marshall,David C.
AU - Simon,Chris
AU - Buckley,Thomas R.
T1 - Accurate Branch Length Estimation in Partitioned Bayesian Analyses Requires Accommodation of Among-Partition Rate Variation and Attention to Branch Length Priors.
PY - 2006
KW -
UR - http://dx.doi.org/10.1080/10635150601087641
N2 - Molecular phylogenetic studies are making increasing use of partitioned Bayesian analyses via software tools like MrBayes version 3. Data partitioning is important because, as long as the same topology/history underlies all of the partitions, it addresses some of the problems associated with the combination of data sets with heterogeneous rates and eliminates the need to argue the validity of tests that have been used to judge data combinability. In addition, new studies indicate that data partitioning and the use of mixed models often dramatically improve the fit of model to data without the cost of overparameterization. While applying partitioned models to studies of protein-coding mitochondrial data, we have found that analyses using MrBayes may infer overly ?long? trees if among-partition rate variation (APRV) is not explicitly accommodated and if data from different partitions evolve at different average rates.
L3 - 10.1080/10635150601087641
JF - Systematic Biology
VL - 55
IS - 6
SP - 993
EP - 1003
ER -
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