Related Experiment Video
Updated: Jan 14, 2026
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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
Published on: March 1, 2022
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Misspecification-robust likelihood-free inference in high dimensions
Owen Thomas1, Raquel Sá-Leão2, Hermínia de Lencastre3,4
1Oslo Centre for Biostatistics and Epidemiology, University of Oslo, Oslo, Norway.
Computational Statistics
|October 27, 2025
Summary
This study introduces a novel method for likelihood-free inference in complex statistical models. The approach enhances computational scalability for high-dimensional parameter spaces, enabling efficient analysis of challenging problems.
Area of Science:
- Statistical Inference
- Computational Statistics
- Machine Learning
Background:
- Likelihood-free inference is crucial for simulator-based models.
- Approximate Bayesian Computation (ABC) struggles with high-dimensional parameters.
- Existing methods face scalability challenges in complex models.
Purpose of the Study:
- To develop an advanced method for likelihood-free inference in high-dimensional parameter spaces.
- To improve the efficiency and scalability of Bayesian optimization-based approaches.
- To enable robust posterior characterization even with model misspecification.
Main Methods:
- An extension of Bayesian optimization to probabilistically approximate discrepancy functions.
- Utilizing separate acquisition functions and summary statistics for parameter subsets.
- Employing an additive acquisition structure with exponentiated loss-likelihood.
Main Results:
- Achieved computational scalability for higher-dimensional parameter spaces.
- Demonstrated efficient inference in moderately sized parameter spaces.
- Outperformed existing modularized ABC methods in comparative analyses.
- Successfully fitted a bacterial transmission model in a 30-dimensional parameter space.
Conclusions:
- The proposed method offers a computationally efficient and scalable solution for likelihood-free inference.
- It provides robust posterior characterization for complex models.
- The approach has practical applications, as shown by the bacterial transmission model analysis.
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