# 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} }

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…

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