5 papers
Nonparametric inference for nonstationary spatial point processes
Izabel Nolau, Flávio B. Gonçalves, Dani Gamerman
Point pattern data often exhibit features such as abrupt changes, hotspots and spatially varying dependence in local intensity. Under a Poisson process framework, these correspond…
A New Perspective on Reverse Diffusion for Monte Carlo Sampling
Jairon H. N. Batista, Flávio B. Gonçalves, Yuri F. Saporito +1
This paper introduces a novel perspective on the use of reverse diffusion processes for sampling from unnormalized densities. The central idea is to embed the target density as the…
Bridging Theory and Practice in Efficient Gaussian Process-Based Statistical Modeling for Large Datasets
Flávio B. Gonçalves, Marcos O. Prates, Gareth O. Roberts
Geostatistics is a branch of statistics concerned with stochastic processes over continuous domains, with Gaussian processes (GPs) providing a flexible and principled modelling fra…
Scalable Bernoulli factories for Bayesian inference with intractable likelihoods
Timothée Stumpf-Fétizon, Flávio B. Gonçalves
Bernoulli factory MCMC algorithms implement accept-reject Markov chains without explicit computation of acceptance probabilities, and are used to target posterior distributions ass…
The Poisson-Gaussian Mixture Process: A Flexible and Robust Approach for Non-Gaussian Geostatistical Modeling
F. B. Gonçalves, M. O. Prates, G. A. S. Aguilar
This paper introduces a novel family of geostatistical models designed to capture complex features beyond the reach of traditional Gaussian processes. The proposed family, termed t…