Fast Bayesian intensity estimation for the permanental process

Date

2017

Authors

Walder, Christian
Bishop, Adrian

Journal Title

Journal ISSN

Volume Title

Publisher

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.

Description

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Citation

Source

Proceedings of the 34th International Conference on Machine Learning, ICML 2017

Type

Conference paper

Book Title

Entity type

Access Statement

Free Access via publisher website

License Rights

DOI

Restricted until

2099-12-31