Void Probabilities and Cauchy-Schwarz Divergence for Generalized Labeled Multi-Bernoulli Models
Crown The generalized labeled multi-Bernoulli (GLMB) is a family of tractable models that alleviates the limitations of the Poisson family in dynamic Bayesian inference of point processes. In this paper, we derive closed form expressions for the void probability functional and the Cauchy-Schwarz div...
| Main Authors: | , , , |
|---|---|
| Format: | Journal Article |
| Published: |
IEEE
2017
|
| Online Access: | http://purl.org/au-research/grants/arc/DP130104404 http://hdl.handle.net/20.500.11937/56063 |
| _version_ | 1848759777556955136 |
|---|---|
| author | Beard, Michael Vo, Ba Tuong Vo, Ba-Ngu Arulampalam, S. |
| author_facet | Beard, Michael Vo, Ba Tuong Vo, Ba-Ngu Arulampalam, S. |
| author_sort | Beard, Michael |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Crown The generalized labeled multi-Bernoulli (GLMB) is a family of tractable models that alleviates the limitations of the Poisson family in dynamic Bayesian inference of point processes. In this paper, we derive closed form expressions for the void probability functional and the Cauchy-Schwarz divergence for GLMBs. The proposed analytic void probability functional is a necessary and sufficient statistic that uniquely characterizes a GLMB, while the proposed analytic Cauchy-Schwarz divergence provides a tractable measure of similarity between GLMBs. We demonstrate the use of both results on a partially observed Markov decision process for GLMBs, with Cauchy-Schwarz divergence based reward, and void probability constraint. |
| first_indexed | 2025-11-14T10:05:17Z |
| format | Journal Article |
| id | curtin-20.500.11937-56063 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:05:17Z |
| publishDate | 2017 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-560632022-10-12T02:38:19Z Void Probabilities and Cauchy-Schwarz Divergence for Generalized Labeled Multi-Bernoulli Models Beard, Michael Vo, Ba Tuong Vo, Ba-Ngu Arulampalam, S. Crown The generalized labeled multi-Bernoulli (GLMB) is a family of tractable models that alleviates the limitations of the Poisson family in dynamic Bayesian inference of point processes. In this paper, we derive closed form expressions for the void probability functional and the Cauchy-Schwarz divergence for GLMBs. The proposed analytic void probability functional is a necessary and sufficient statistic that uniquely characterizes a GLMB, while the proposed analytic Cauchy-Schwarz divergence provides a tractable measure of similarity between GLMBs. We demonstrate the use of both results on a partially observed Markov decision process for GLMBs, with Cauchy-Schwarz divergence based reward, and void probability constraint. 2017 Journal Article http://hdl.handle.net/20.500.11937/56063 10.1109/TSP.2017.2723355 http://purl.org/au-research/grants/arc/DP130104404 IEEE restricted |
| spellingShingle | Beard, Michael Vo, Ba Tuong Vo, Ba-Ngu Arulampalam, S. Void Probabilities and Cauchy-Schwarz Divergence for Generalized Labeled Multi-Bernoulli Models |
| title | Void Probabilities and Cauchy-Schwarz Divergence for Generalized Labeled Multi-Bernoulli Models |
| title_full | Void Probabilities and Cauchy-Schwarz Divergence for Generalized Labeled Multi-Bernoulli Models |
| title_fullStr | Void Probabilities and Cauchy-Schwarz Divergence for Generalized Labeled Multi-Bernoulli Models |
| title_full_unstemmed | Void Probabilities and Cauchy-Schwarz Divergence for Generalized Labeled Multi-Bernoulli Models |
| title_short | Void Probabilities and Cauchy-Schwarz Divergence for Generalized Labeled Multi-Bernoulli Models |
| title_sort | void probabilities and cauchy-schwarz divergence for generalized labeled multi-bernoulli models |
| url | http://purl.org/au-research/grants/arc/DP130104404 http://hdl.handle.net/20.500.11937/56063 |