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dc.contributor.authorDam, N.
dc.contributor.authorPhung, D.
dc.contributor.authorVo, Ba-Ngu
dc.contributor.authorHuynh, V.
dc.date.accessioned2018-05-14T06:08:41Z
dc.date.available2018-05-14T06:08:41Z
dc.date.created2018-05-13T00:32:01Z
dc.date.issued2018
dc.identifier.citationDam, N. and Phung, D. and Vo, B. and Huynh, V. 2018. Forward-Backward smoothing for hidden markov models of point pattern data, pp. 252-261.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/66625
dc.identifier.doi10.1109/DSAA.2017.78
dc.description.abstract

© 2017 IEEE. This paper considers a discrete-time sequential latent model for point pattern data, specifically a hidden Markov model (HMM) where each observation is an instantiation of a random finite set (RFS). This so-called RFS-HMM is worthy of investigation since point pattern data are ubiquitous in artificial intelligence and data science. We address the three basic problems typically encountered in such a sequential latent model, namely likelihood computation, hidden state inference, and parameter estimation. Moreover, we develop algorithms for solving these problems including forward-backward smoothing for likelihood computation and hidden state inference, and expectation-maximisation for parameter estimation. Simulation studies are used to demonstrate key properties of RFS-HMM, whilst real data in the domain of human dynamics are used to demonstrate its applicability.

dc.relation.sponsoredbyhttp://purl.org/au-research/grants/arc/DP160104662
dc.titleForward-Backward smoothing for hidden markov models of point pattern data
dc.typeConference Paper
dcterms.source.volume2018-January
dcterms.source.startPage252
dcterms.source.endPage261
dcterms.source.titleProceedings - 2017 International Conference on Data Science and Advanced Analytics, DSAA 2017
dcterms.source.seriesProceedings - 2017 International Conference on Data Science and Advanced Analytics, DSAA 2017
dcterms.source.isbn9781509050048
curtin.departmentSchool of Electrical Engineering, Computing and Mathematical Science (EECMS)
curtin.accessStatusFulltext not available


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