Efficient Bayesian Inference of Sigmoidal Gaussian Cox Processes

Journal of Machine Learning Research 2018 Christian DonnerManfred Opper

We present an approximate Bayesian inference approach for estimating the intensity of an inhomogeneous Poisson process, where the intensity function is modelled using a Gaussian process (GP) prior via a sigmoid link function. Augmenting the model using a latent marked Poisson process and P\'olya--Gamma random variables we obtain a representation of the likelihood which is conjugate to the GP prior... (read more)

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