Fast Bayesian intensity estimation for the permanental process
- Publication Type:
- Conference Proceeding
- Citation:
- 34th International Conference on Machine Learning, ICML 2017, 2017, 7 pp. 5459 - 5471
- Issue Date:
- 2017-01-01
Closed Access
Filename | Description | Size | |||
---|---|---|---|---|---|
walder17a.pdf | Published version | 2.69 MB | |||
1701.03535v3.pdf | Accepted Manuscript version | 4.89 MB |
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
Copyright © 2017 by the authors. 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.
Please use this identifier to cite or link to this item: