Exact and computationally efficient Bayesian inference for generalized Markov modulated Poisson processes

TL;DR

This paper introduces an exact Bayesian inference method for a new class of Markov-modulated Cox processes, enabling efficient analysis of large point pattern datasets without time discretization.

Contribution

It proposes a novel unidimensional Cox process model with Markov switching intensities and an exact MCMC-based Bayesian inference approach that is computationally efficient.

Findings

The method accurately infers model parameters from simulated data.

Applied to epidemic data, it effectively models Dengue Fever and COVID-19 outbreaks.

The approach avoids discretization errors, ensuring precise inference.

Abstract

Statistical modeling of point patterns is an important and common problem in several areas. The Poisson process is the most common process used for this purpose, in particular, its generalization that considers the intensity function to be stochastic. This is called a Cox process and different choices to model the dynamics of the intensity gives rise to a wide range of models. We present a new class of unidimensional Cox process models in which the intensity function assumes parametric functional forms that switch among them according to a continuous-time Markov chain. A novel methodology is proposed to perform exact Bayesian inference based on MCMC algorithms. The term exact refers to the fact that no discrete time approximation is used and Monte Carlo error is the only source of inaccuracy. The reliability of the algorithms depends on a variety of specifications which are carefully addressed, resulting in a computationally efficient (in terms of computing time) algorithm and enabling its use with large data sets. Simulated and real examples are presented to illustrate the efficiency and applicability of the proposed methodology. A specific model to fit epidemic curves is proposed and used to analyze data from Dengue Fever in Brazil and COVID-19 in some countries.