Simulation-based Bayesian inference with ameliorative learned summary statistics -- Part I

TL;DR

This paper introduces a simulation-based Bayesian inference method using learned summary statistics and a transformation technique to improve inference accuracy, especially for complex or intractable likelihoods, and supports distributed computing for large datasets.

Contribution

It proposes a novel transformation technique leveraging the Cressie-Read discrepancy for summarizing learned statistics, enhancing Bayesian inference in complex simulation models.

Findings

Effective summarization of learned statistics preserves inference power.

Framework extends to weakly dependent data scenarios.

Suitable for distributed computing with large datasets.

Abstract

This paper, which is Part 1 of a two-part paper series, considers a simulation-based inference with learned summary statistics, in which such a learned summary statistic serves as an empirical-likelihood with ameliorative effects in the Bayesian setting, when the exact likelihood function associated with the observation data and the simulation model is difficult to obtain in a closed form or computationally intractable. In particular, a transformation technique which leverages the Cressie-Read discrepancy criterion under moment restrictions is used for summarizing the learned statistics between the observation data and the simulation outputs, while preserving the statistical power of the inference. Here, such a transformation of data-to-learned summary statistics also allows the simulation outputs to be conditioned on the observation data, so that the inference task can be performed over certain sample sets of the observation data that are considered as an empirical relevance or believed to be particular importance. Moreover, the simulation-based inference framework discussed in this paper can be extended further, and thus handling weakly dependent observation data. Finally, we remark that such an inference framework is suitable for implementation in distributed computing, i.e., computational tasks involving both the data-to-learned summary statistics and the Bayesian inferencing problem can be posed as a unified distributed inference problem that will exploit distributed optimization and MCMC algorithms for supporting large datasets associated with complex simulation models.