Sequential inference methods for non-homogeneous poisson processes with state-space prior
Accepted version
Peer-reviewed
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Repository DOI
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Authors
Li, Chenhao https://orcid.org/0000-0001-6731-3675
Godsill, SJ
Abstract
© 2018 IEEE. The Non-homogeneous Poisson process is a point process with time-varying intensity across its domain, the use of which arises in numerous areas in signal processing and machine learning. However, applications are largely limited by the intractable likelihood function and the high computational cost of existing inference schemes. We present a sequential inference framework that utilises generative Poisson data and sequential Markov Chain Monte Carlo (SMCMC) algorithm to enable online inference in various applications. The proposed model is compared to competing methods on synthetic datasets and tested with real-world financial data.
Description
Keywords
Bayesian inference, non-homogeneous Poisson process, state-space model, sequential-MCMC
Journal Title
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Conference Name
ICASSP 2018 - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Journal ISSN
1520-6149
Volume Title
2018-April
Publisher
IEEE
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All rights reserved