The effectiveness of Bayesian Additive Regression Trees (BART) has been demonstrated in a variety of contexts including non parametric regression and classification. Here we introduce a BART scheme for estimating the intensity of inhomogeneous Poisson Processes. Poisson intensity estimation is a vital task in various applications including medical imaging, astrophysics and network traffic analysis. Our approach enables full posterior inference of the intensity in a nonparametric regression setting. We demonstrate the performance of our scheme through simulation studies on synthetic and real datasets in one and two dimensions, and compare our approach to alternative approaches.
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