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SEQUENTIAL INFERENCE METHODS FOR NON-HOMGENEOUS POISSON PROCESSES WITH STATE-SPACE PRIOR

Citation Author(s):
Chenhao Li, Simon J. Godsill
Submitted by:
Chenhao Li
Last updated:
20 April 2018 - 4:06am
Document Type:
Poster
Document Year:
2018
Event:
Presenters:
Chenhao Li
Paper Code:
4077
 

The non-homogeneous Poisson process (NHPP) is a point process with time-varying intensity across its domain, the use of which arises in numerous domains in signal processing, machine learning and many other fields. However, its applications are largely limited by the intractable likelihood and the high computational cost of existing inference schemes. We present an online inference framework that utilises generative Poisson data and sequential Markov Chain Monte Carlo (SMCMC) algorithm, which achieves improved performance in both synthetic and real datasets.

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