39 citations · 60 across the 6 of their papers we have counts for
5 papers · 1 filter
A Bayesian Joint Model for Multiple Point Processes with Application to Presence-Only Data
Laura C. M. Teixeira, Dani Gamerman, Vinicius Peripato +1
Joint modeling of multiple point processes is relevant in applications where relationships among processes are of interest, such as in ecological and archaeological studies. Statis…
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…
Exact Bayesian Inference for Geostatistical Models under Preferential Sampling
Douglas Mateus da Silva, Dani Gamerman
Preferential sampling is a common feature in geostatistics and occurs when the locations to be sampled are chosen based on information about the phenomena under study. In this case…
A semiparametric approach for bivariate extreme exceedances
Manuele Leonelli, Dani Gamerman
Inference over tails is performed by applying only the results of extreme value theory. Whilst such theory is well defined and flexible enough in the univariate case, multivariate…
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…