A Bayesian Joint Model for Multiple Point Processes with Application to Presence-Only Data

Joint modeling of multiple point processes is relevant in applications where relationships among processes are of interest, such as in ecological and archaeological studies.

Statistical inference becomes particularly challenging when multiple processes are analyzed jointly and the observed data correspond to presence-only patterns, which are subject to preferential sampling and partial observability.

This paper proposes a Bayesian joint model for multiple point processes, with application to the presence-only setting.

The dependence between processes is explicitly incorporated into the probabilistic specification of the model using Bayesian networks.

Direct use of the likelihood leads to intractable likelihood functions.

Latent data processes are then introduced so that the augmented likelihood function becomes tractable and can be exactly evaluated.

This formulation also enables direct inference on the number and the spatial distribution of unobserved occurrences of any of the point patterns.

Inference is carried out using Markov chain Monte Carlo with blocked Gibbs sampling.

Simulation studies demonstrate that the proposed inferential scheme is able to recover the true model parameters.

The proposed model is applied to real presence-only data of archaeological sites and tree species from Amazonia, as part of the study of the effect that pre-Columbian Indigenous presence might have on the occurrences of relevant tree species.

The results are consistent with the findings reported in the literature.

They also illustrate how the proposed model enables inference on the existence and on the magnitude of the relation between processes, in addition to their association with environmental covariates.