Bayesian optimization using sequential Monte Carlo

TL;DR

This paper introduces a Bayesian optimization method that employs Sequential Monte Carlo techniques to efficiently compute posterior distributions, enabling better sequential decision-making for optimizing continuous functions.

Contribution

It proposes a novel Bayesian optimization framework using SMC to accurately approximate posterior distributions, improving the selection of evaluation points.

Findings

SMC-based Bayesian optimization effectively estimates posteriors.

The method enhances the efficiency of sequential function evaluations.

Results demonstrate improved optimization performance over traditional methods.

Abstract

We consider the problem of optimizing a real-valued continuous function $f$ using a Bayesian approach, where the evaluations of $f$ are chosen sequentially by combining prior information about $f$, which is described by a random process model, and past evaluation results. The main difficulty with this approach is to be able to compute the posterior distributions of quantities of interest which are used to choose evaluation points. In this article, we decide to use a Sequential Monte Carlo (SMC) approach.