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Comparing partitioned models to mixture models: do information criteria apply?


Crotty, SM and Holland, BR, Comparing partitioned models to mixture models: do information criteria apply?, Systematic Biology, 71, (6) pp. 1541-1548. ISSN 1063-5157 (2022) [Refereed Article]

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Copyright Statement

The Author(s) 2022. Published by Oxford University Press on behalf of the Society of Systematic Biologists. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (, which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited.

DOI: doi:10.1093/sysbio/syac003


The use of information criteria to distinguish between phylogenetic models has become ubiquitous within the field. However, the variety and complexity of available models are much greater now than when these practices were established. The literature shows an increasing trajectory of healthy skepticism with regard to the use of information theory-based model selection within phylogenetics. We add to this by analyzing the specific case of comparison between partition and mixture models. We argue from a theoretical basis that information criteria are inherently more likely to favor partition models over mixture models, and we then demonstrate this through simulation. Based on our findings, we suggest that partition and mixture models are not suitable for information-theory based model comparison.

Item Details

Item Type:Refereed Article
Keywords:AIC, BIC, information criteria, maximum likelihood, mixture models, partitioned model, phylogenetics
Research Division:Mathematical Sciences
Research Group:Applied mathematics
Research Field:Biological mathematics
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in the mathematical sciences
UTAS Author:Holland, BR (Professor Barbara Holland)
ID Code:149251
Year Published:2022
Web of Science® Times Cited:3
Deposited By:Mathematics
Deposited On:2022-03-21
Last Modified:2023-01-09
Downloads:4 View Download Statistics

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