Exact Bayesian Inference in Spatiotemporal Cox Processes Driven by Multivariate Gaussian Processes

DOI10.1111/rssb.12237OpenAlexW3103714082MaRDI QIDQ4603818

Dani Gamerman, Flávio B. Gonçalves

Publication date: 19 February 2018

Published in: Journal of the Royal Statistical Society Series B: Statistical Methodology (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1504.06638

zbMATH Keywords

point patternintractable likelihoodMarkov chain Monte Carlo samplingaugmented modeldynamic Gaussian process

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Gaussian processes (60G15) Bayesian inference (62F15) Point processes (e.g., Poisson, Cox, Hawkes processes) (60G55)

Related Items (8)

Spatial Cox processes in an infinite-dimensional framework ⋮ Bayesian computation for Log-Gaussian Cox processes: a comparative analysis of methods ⋮ Fast, Approximate Maximum Likelihood Estimation of Log-Gaussian Cox Processes ⋮ Log-Gaussian Cox process modeling of large spatial lightning data using spectral and Laplace approximations ⋮ Scalable inference for space‐time Gaussian Cox processes ⋮ Analysis of presence-only data via exact Bayes, with model and effects identification ⋮ Spatiotemporal point processes: regression, model specifications and future directions ⋮ Exact and computationally efficient Bayesian inference for generalized Markov modulated Poisson processes

Uses Software

• spBayes
• lgcp

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