A simulated annealing procedure based on the ABC Shadow algorithm for statistical inference of point processes

TL;DR

This paper introduces a global optimization method based on the ABC Shadow algorithm for statistical inference of point processes, demonstrating its effectiveness on simulated and real datasets.

Contribution

It presents a novel optimization procedure leveraging ABC Shadow dynamics applicable to differentiable probability densities for point process inference.

Findings

Effective on simulated data

Successful application to real data

General method for differentiable densities

Abstract

Recently a new algorithm for sampling posteriors of unnormalised probability densities, called ABC Shadow, was proposed in [8]. This talk introduces a global optimisation procedure based on the ABC Shadow simulation dynamics. First the general method is explained, and then results on simulated and real data are presented. The method is rather general, in the sense that it applies for probability densities that are continuously differentiable with respect to their parameters