6 papers
Hierarchical Besov-Laplace priors for spatially inhomogeneous binary classification
Patric Dolmeta, Matteo Giordano
We study nonparametric Bayesian binary classification, in the case where the unknown probability response function is possibly spatially inhomogeneous, for example, being generally…
Increasing domain asymptotics for covariate-based nonparametric Bayesian intensity estimation with Gaussian and Besov-Laplace priors
Patric Dolmeta, Matteo Giordano
We study the problem of estimating the intensity function of a covariate-driven point process based on observations of the points and covariates over a large window. We consider th…
A nonparametric Bayesian analysis of independent and identically distributed observations of covariate-driven Poisson processes
Patric Dolmeta, Matteo Giordano
An important task in the statistical analysis of inhomogeneous point processes is to investigate the influence of a set of covariates on the point-generating mechanism. In this art…
Bayesian inference with Besov-Laplace priors for spatially inhomogeneous binary classification surfaces
Matteo Giordano
In this article, we study the binary classification problem with supervised data, in the case where the covariate-to-probability-of-success map is possibly spatially inhomogeneous.…
Gaussian Process Methods for Covariate-Based Intensity Estimation
Patric Dolmeta, Matteo Giordano
We study nonparametric Bayesian inference for the intensity function of a covariate-driven point process. We extend recent results from the literature, showing that a wide class of…
Nonparametric Bayesian intensity estimation for covariate-driven inhomogeneous point processes
Matteo Giordano, Alisa Kirichenko, Judith Rousseau
This work studies nonparametric Bayesian estimation of the intensity function of an inhomogeneous Poisson point process in the important case where the intensity depends on covaria…