Identifiability of Parameters in MCMC Bayesian Inference of Phylogeny

Rannala, Bruce

doi:10.1080/10635150290102429

Cited by 190Genomics and Phylogenetic StudiesMarkov Chains and Monte Carlo MethodsGenetic diversity and population structure

Identifiability of Parameters in MCMC Bayesian Inference of Phylogeny

Rannala, Bruce·2002·Systematic Biology

DOI 10.1080/10635150290102429

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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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Modelling too many parameters can lead to a loss of power to distinguish between variants of a model.
“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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Identi�ability of Parameters in MCMC Bayesian Inference
Phylogeny, O., & Rannala, B. H. (2013)
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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
DOI: 10.1080/10635150290102429
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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Identifiability of Parameters in MCMC Bayesian Inference of Phylogeny

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