Fast Bayesian intensity estimation for the permanental process
Date
2017
Authors
Walder, Christian
Bishop, Adrian
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International Machine Learning Society
Abstract
The Cox process is a stochastic process which generalises the Poisson process by letting the underlying intensity function itself be a stochastic process. In this paper we present a fast Bayesian inference scheme for the permanental process, a Cox process under which the square root of the intensity is a Gaussian process. In particular we exploit connections with reproducing kernel Hilbert spaces, to derive efficient approximate Bayesian inference algorithms based on the Laplace approximation to the predictive distribu-tion and marginal likelihood. We obtain a simple algorithm which we apply to toy and real-world problems, obtaining orders of magnitude speed improvements over previous work.
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Proceedings of the 34th International Conference on Machine Learning, ICML 2017
Type
Conference paper
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Free Access via publisher website
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2099-12-31