The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Identifiability of Parameters in MCMC Bayesian Inference of Phylogeny
Abstract
Methods for Bayesian inference of phylogeny using DNA sequences based on Markov chain Monte Carlo (MCMC) techniques allow the incorporation of arbitrarily complex models of the DNA substitution process, and other aspects of evolution. This has increased the realism of models, potentially improving the accuracy of the methods, and is largely responsible for their recent popularity. Another consequence of the increased complexity of models in Bayesian phylogenetics is that these models have, in several cases, become overparameterized. In such cases, some parameters of the model are not identifiable; different combinations of nonidentifiable parameters lead to the same likelihood, making it impossible to decide among the potential parameter values based on the data. Overparameterized models can also slow the rate of convergence of MCMC algorithms due to large negative correlations among parameters in the posterior probability distribution. Functions of parameters can sometimes be found, in overparameterized models, that are identifiable, and inferences based on these functions are legitimate. Examples are presented of overparameterized models that have been proposed in the context of several Bayesian methods for inferring the relative ages of nodes in a phylogeny when the substitution rate evolves over time.
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“In addition, modelling too many parameters can lead to a loss of power to distinguish between variants of a model 59 .”
From The modern molecular clock (2003) · cites this paper as [59]
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Equation of State Calculations by Fast Computing Machines
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953) · The Journal of Chemical Physics · doi:10.1063/1.1699114 · Cited by 37,192
Monte Carlo sampling methods using Markov chains and their applications.
Hastings, W. K. (1970) · Biometrika · doi:10.1093/biomet/57.1.97 · Cited by 15,195
Casella, G., & Berger, R. L. (1993) · Chapman & Hall/CRC eBooks · doi:10.1201/9781003456285 · Cited by 5,835
Bayes and Empirical Bayes Methods for Data Analysis
Carlin, B. P., & Louis, T. A. (2001) · Technometrics · doi:10.1198/tech.2001.s608 · Cited by 2,110
Huber, P. J. (1997) · Springer series in statistics · doi:10.1007/978-1-4612-0667-5_6 · Cited by 1,569
Monte Carlo Sampling Methods Using Markov Chains and Their Applications
Hastings, W. K. (1970) · Biometrika · doi:10.2307/2334940 · Cited by 1,526
Estimating the rate of evolution of the rate of molecular evolution
Thorne, J. L., Kishino, H., & Painter, I. S. (1998) · Molecular Biology and Evolution · doi:10.1093/oxfordjournals.molbev.a025892 · Cited by 1,317
Bayesian phylogenetic inference using DNA sequences: a Markov Chain Monte Carlo Method
Yang, Z., & Rannala, B. (1997) · Molecular Biology and Evolution · doi:10.1093/oxfordjournals.molbev.a025811 · Cited by 1,274
Self-citationA nonparametric approach to estimating divergence times in the absence of rate constancy.
Sanderson, M. J. (1997) · Molecular Biology and Evolution · doi:10.1093/oxfordjournals.molbev.a025731 · Cited by 1,156
Introducing Markov chain Monte Carlo
Gilks, W., Richardson, S., & Spiegelhalter, D. (1995) · Chapman & Hall/CRC eBooks · doi:10.1201/b14835-6 · Cited by 671
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Metadata
- Authors
- Rannala, B.
- Affiliations
- University of Alberta (CA)
- Corresponding author
- Rannala, Bruce
- ORCID
- Rannala, Bruce
- Venue
- Systematic Biology
- Publisher
- Oxford University Press (OUP)
- Published
- 2002-09-01
- Volume
- 51
- Issue
- 5
- Pages
- 754-760
- ISSN
- 1063-5157 (ISSN-L)
- 1076-836X
- OpenAlex
- W2107808362
- Cited by
- 190
- References
- 17
- Citations by year
- 2026: 4 · 2025: 2 · 2024: 4 · 2023: 2 · 2022: 8 · 2021: 6 · 2020: 4 · 2019: 16 · 2018: 7 · 2017: 5 · 2016: 5 · 2015: 9 · 2014: 23 · 2013: 8 · 2012: 13
- FWCI
- 2.09
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Systematic Biology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health
- National Human Genome Research Institute (HG 01988, R01HG001988)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Physical Sciences › Mathematics › Statistics and Probability › Markov Chains and Monte Carlo Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic diversity and population structure
- Keywords
- parameter identifiability, overparameterized models, Bayesian phylogenetics, MCMC convergence, node age estimation
- MeSH terms
- Algorithms, Bayes Theorem, Biological Evolution, Markov Chains, Models, Genetic, Monte Carlo Method, Phylogeny, Time Factors, Evolution, Molecular
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