Guided sequential ABC schemes for intractable Bayesian models

TL;DR

This paper introduces guided proposal samplers for sequential ABC methods that improve efficiency and convergence by focusing sampling on plausible posterior regions, especially in complex models.

Contribution

It presents novel guided Gaussian and copula-based proposal schemes for SIS-ABC and SMC-ABC, enhancing inference speed and accuracy in challenging Bayesian models.

Findings

Speeds up convergence of sequential ABC algorithms

Reduces computational effort in complex models

Maintains inference accuracy with guided proposals

Abstract

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.