• Corpus ID: 55427366

# Fast and scalable non-parametric Bayesian inference for Poisson point processes

@article{Gugushvili2018FastAS,
title={Fast and scalable non-parametric Bayesian inference for Poisson point processes},
author={Shota Gugushvili and Frank van der Meulen and Moritz Schauer and Peter Spreij},
journal={arXiv: Methodology},
year={2018}
}
• Published 10 April 2018
• Mathematics, Computer Science
• arXiv: Methodology
We study the problem of non-parametric Bayesian estimation of the intensity function of a Poisson point process. The observations are assumed to be $n$ independent realisations of a Poisson point process on the interval $[0,T]$. We propose two related approaches. In both approaches we model the intensity function as piecewise constant on $N$ bins forming a partition of the interval $[0,T]$. In the first approach the coefficients of the intensity function are assigned independent Gamma priors…
8 Citations

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