FiEstAS sampling -- a Monte Carlo algorithm for multidimensional numerical integration

TL;DR

This paper introduces FiEstAS sampling, a Monte Carlo algorithm designed for efficient multidimensional numerical integration, especially effective for complex Bayesian problems with multimodal distributions and parameter degeneracies.

Contribution

The paper presents a novel Monte Carlo integration method based on FiEstAS, improving performance in challenging Bayesian analysis scenarios with multimodal and degenerate distributions.

Findings

Effective in handling multimodal distributions

Performs well with strong parameter degeneracies

Source code available for implementation

Abstract

This paper describes a new algorithm for Monte Carlo integration, based on the Field Estimator for Arbitrary Spaces (FiEstAS). The algorithm is discussed in detail, and its performance is evaluated in the context of Bayesian analysis, with emphasis on multimodal distributions with strong parameter degeneracies. Source code is available upon request.