Forward-Backward smoothing for hidden markov models of point pattern data
© 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 ubiquitou...
| Main Authors: | , , , |
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| Format: | Conference Paper |
| Published: |
2018
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| Online Access: | http://purl.org/au-research/grants/arc/DP160104662 http://hdl.handle.net/20.500.11937/66625 |
| _version_ | 1848761355205607424 |
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| author | Dam, N. Phung, D. Vo, Ba-Ngu Huynh, V. |
| author_facet | Dam, N. Phung, D. Vo, Ba-Ngu Huynh, V. |
| author_sort | Dam, N. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 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. |
| first_indexed | 2025-11-14T10:30:21Z |
| format | Conference Paper |
| id | curtin-20.500.11937-66625 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:30:21Z |
| publishDate | 2018 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-666252022-10-27T06:23:26Z Forward-Backward smoothing for hidden markov models of point pattern data Dam, N. Phung, D. Vo, Ba-Ngu Huynh, V. © 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. 2018 Conference Paper http://hdl.handle.net/20.500.11937/66625 10.1109/DSAA.2017.78 http://purl.org/au-research/grants/arc/DP160104662 restricted |
| spellingShingle | Dam, N. Phung, D. Vo, Ba-Ngu Huynh, V. Forward-Backward smoothing for hidden markov models of point pattern data |
| title | Forward-Backward smoothing for hidden markov models of point pattern data |
| title_full | Forward-Backward smoothing for hidden markov models of point pattern data |
| title_fullStr | Forward-Backward smoothing for hidden markov models of point pattern data |
| title_full_unstemmed | Forward-Backward smoothing for hidden markov models of point pattern data |
| title_short | Forward-Backward smoothing for hidden markov models of point pattern data |
| title_sort | forward-backward smoothing for hidden markov models of point pattern data |
| url | http://purl.org/au-research/grants/arc/DP160104662 http://hdl.handle.net/20.500.11937/66625 |