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Updated: May 29, 2026
08:12
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
Published on: March 1, 2022
Lack of confidence in approximate Bayesian computation model choice.
Christian P Robert1, Jean-Marie Cornuet, Jean-Michel Marin
1Université Paris-Dauphine, 75775 Paris cedex 16, France. Christian.Robert@ceremade.dauphine.fr
Summary
Approximate Bayesian computation (ABC) is useful for complex models, but theoretical support for its use in model choice is lacking. Empirical verification is crucial for reliable results in phylogenetic analysis.
Area of Science:
- Computational Biology
- Statistical Modeling
- Phylogenetics
Background:
- Approximate Bayesian computation (ABC) is widely used for analyzing complex stochastic models.
- Previous work suggested ABC was legitimate for model choice in Gibbs random fields.
- ABC has been implemented for phylogenetic models in the DIY-ABC software.
Purpose of the Study:
- To present arguments questioning the theoretical basis of ABC for model choice.
- To highlight the information loss due to insufficient summary statistics in ABC algorithms.
- To emphasize the need for empirical validation of ABC model choice procedures.
Main Methods:
- Reviewing theoretical arguments for ABC model choice.
- Analyzing the impact of summary statistics on information loss.
- Discussing the implementation of ABC in the DIY-ABC software for phylogenetic models.
Main Results:
- Theoretical arguments for ABC model choice are found to be missing.
- ABC algorithms can incur unknown information loss from insufficient summary statistics.
- The approximation error in ABC model choice may not correlate with computational effort.
Conclusions:
- The theoretical justification for using ABC for model choice is currently lacking.
- Empirical verification of ABC performance is essential for reliable model selection.
- Further validation, similar to that in DIY-ABC, is necessary for robust phylogenetic model choice.
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