A note on Bayesian logistic regression for spatial exponential family Gibbs point processes

TL;DR

This paper introduces a Bayesian inference method for spatial point processes by combining logistic regression with quadratic tangential variational approximation, demonstrated through numerical examples.

Contribution

It presents a novel Bayesian approach for analyzing spatial point patterns by integrating logistic regression with variational approximation techniques.

Findings

Effective Bayesian analysis of spatial point patterns

Demonstrated on numerical examples showing practical utility

Advances inference methods for spatial exponential family Gibbs processes

Abstract

Recently, a very attractive logistic regression inference method for exponential family Gibbs spatial point processes was introduced. We combined it with the technique of quadratic tangential variational approximation and derived a new Bayesian technique for analysing spatial point patterns. The technique is described in detail, and demonstrated on numerical examples.