Guided sequential ABC schemes for intractable Bayesian models
Journal article, 2024

Sequential algorithms such as sequential importance sampling (SIS) and sequential Monte Carlo (SMC) have proven fundamental in Bayesian inference for models not admitting a readily available likelihood function. For approximate Bayesian computation (ABC), SMC-ABC is the state-of-art sampler. However, since the ABC paradigm is intrinsically wasteful, sequential ABC schemes can benefit from well-targeted proposal samplers that efficiently avoid improbable parameter regions. We contribute to the ABC modeller’s toolbox with novel proposal samplers that are conditional to summary statistics of the data. In a sense, the proposed parameters are “guided” to rapidly reach regions of the posterior surface that are compatible with the observed data. This speeds up the convergence of these sequential samplers, thus reducing the computational effort, while preserving the accuracy in the inference. We provide a variety of guided Gaussian and copula-based samplers for both SIS-ABC and SMC-ABC easing inference for challenging case-studies, including multimodal posteriors, highly correlated posteriors, hierarchical models with about 20 parameters, and a simulation study of cell movements using more than 400 summary statistics.

simulation-based inference

Approximate Bayesian computation

copulas

sequential importance sampling

sequential Monte Carlo

Author

Umberto Picchini

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

Massimiliano Tamborrino

The University of Warwick

Bayesian Analysis

1936-0975 (ISSN) 1931-6690 (eISSN)

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Chalmers AI Research Centre (CHAIR), 2020-01-01 -- 2024-12-31.

Swedish Research Council (VR) (2019-03924), 2020-01-01 -- 2023-12-31.

Statistical Inference and Stochastic Modelling of Protein Folding

Swedish Research Council (VR) (2013-5167), 2014-01-01 -- 2019-12-31.

Subject Categories

Probability Theory and Statistics

DOI

10.1214/24-BA1451

More information

Latest update

7/6/2024 2