39 citations · 40 across the 10 of their papers we have counts for
14 papers
From Shallow Bayesian Neural Networks to Gaussian Processes: General Convergence, Identifiability and Scalable Inference
Gracielle Antunes de Araújo, Flávio B. Gonçalves
In this work, we study scaling limits of shallow Bayesian neural networks (BNNs) via their connection to Gaussian processes (GPs), with an emphasis on statistical modeling, identif…
A Reverse-BSDE Diffusion Sampler
Jairon H. N. Batista, Flávio B. Gonçalves, Yuri F. Saporito +1
Diffusion-based generative models have renewed interest in stochastic differential equation methods for sampling from complex distributions. We study a setting in which the target…
Scalable Bernoulli Factory MCMC for Intractable Marginalised Posteriors
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…
Exact Bayesian inference for level-set Cox processes with piecewise constant intensity function
Flavio B. Gonçalves, Barbara C. C. Dias
This paper proposes a new methodology to perform Bayesian inference for a class of multidimensional Cox processes in which the intensity function is piecewise constant. Poisson pro…
Exact Bayesian inference for diffusion driven Cox processes
Flavio B. Gonçalves, Krzysztof G. Łatuszyński, Gareth O. Roberts
In this paper, we present a novel methodology to perform Bayesian inference for Cox processes in which the intensity function is driven by a diffusion process. The novelty lies in…
Exact and computationally efficient Bayesian inference for generalized Markov modulated Poisson processes
Flavio B. Gonçalves, Livia M. Dutra, Roger W. C. Silva
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…
Flexible Bayesian modelling in dichotomous item response theory using mixtures of skewed item curves
Flávio B. Gonçalves, Juliane Venturelli, Rosangela H. Loschi
Most Item Response Theory (IRT) models for dichotomous responses are based on probit or logit link functions which assume a symmetric relationship between the probability of a corr…
Bayesian linear regression models with flexible error distributions
Nívea B. da Silva, Marcos O. Prates, Flávio B. Gonçalves
This work introduces a novel methodology based on finite mixtures of Student-t distributions to model the errors' distribution in linear regression models. The novelty lies on a pa…
Barker's algorithm for Bayesian inference with intractable likelihoods
Flavio B. Gonçalves, Krzysztof Łatuszyński, Gareth O. Roberts
In this expository paper we abstract and describe a simple MCMC scheme for sampling from intractable target densities. The approach has been introduced in Gonçalves et al. (2017a)…
Identifying down and up-regulated chromosome regions using RNA-Seq data
Vinícius D. Mayrink, Flávio B. Gonçalves
The number of studies dealing with RNA-Seq data analysis has experienced a fast increase in the past years making this type of gene expression a strong competitor to the DNA microa…
Exact Monte Carlo likelihood-based inference for jump-diffusion processes
Flávio B. Gonçalves, Krzysztof G. Łatuszyński, Gareth O. Roberts
Statistical inference for discretely observed jump-diffusion processes is a complex problem which motivates new methodological challenges. Thus existing approaches invariably resor…
A Bayesian hidden Markov mixture model to detect overexpressed chromosome regions
Vinícius Diniz Mayrink, Flávio Bambirra Gonçalves
In this study, we propose a hidden Markov mixture model for the analysis of gene expression measurements mapped to chromosome locations. These expression values represent preproces…
Robust Bayesian model selection for heavy-tailed linear regression using finite mixtures
Flávio B Gonçalves, Marcos O. Prates, Victor H. Lachos
In this paper we present a novel methodology to perform Bayesian model selection in linear models with heavy-tailed distributions. We consider a finite mixture of distributions to…
Exact Bayesian inference in spatio-temporal Cox processes driven by multivariate Gaussian processes
Flávio B. Gonçalves, Dani Gamerman
In this paper we present a novel inference methodology to perform Bayesian inference for spatiotemporal Cox processes where the intensity function depends on a multivariate Gaussia…