Rao-blackwellised particle filtering via data augmentation
In this paper, we extend the Rao-Blackwellised particle filtering method t o more complex hybrid models consisting of Gaussian latent variables and discrete observations. This is accomplished by augmenting the models with artificial variables that enable us to apply Rao-Blackwellisation. Other impro...
| Auteurs principaux: | , , |
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| Format: | Conference item |
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Neural information processing systems foundation
2002
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| _version_ | 1826280614083428352 |
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| author | Andrieu, C De Freitas, N Doucet, A |
| author_facet | Andrieu, C De Freitas, N Doucet, A |
| author_sort | Andrieu, C |
| collection | OXFORD |
| description | In this paper, we extend the Rao-Blackwellised particle filtering method t o more complex hybrid models consisting of Gaussian latent variables and discrete observations. This is accomplished by augmenting the models with artificial variables that enable us to apply Rao-Blackwellisation. Other improvements include the design of an optimal importance proposal distribution and being able to swap the sampling an selection steps to handle out liers. We focus on sequent ial binary classifiers t hat consist of linear- combinations of basis functions, whose coefficients evolve according t o a Gaussian smoothness prior. Our results show significant improvements. |
| first_indexed | 2024-03-07T00:16:19Z |
| format | Conference item |
| id | oxford-uuid:7af73b5e-34f9-4326-8f5f-7fdf6fa0242a |
| institution | University of Oxford |
| last_indexed | 2024-03-07T00:16:19Z |
| publishDate | 2002 |
| publisher | Neural information processing systems foundation |
| record_format | dspace |
| spelling | oxford-uuid:7af73b5e-34f9-4326-8f5f-7fdf6fa0242a2022-03-26T20:47:35ZRao-blackwellised particle filtering via data augmentationConference itemhttp://purl.org/coar/resource_type/c_5794uuid:7af73b5e-34f9-4326-8f5f-7fdf6fa0242aSymplectic Elements at OxfordNeural information processing systems foundation2002Andrieu, CDe Freitas, NDoucet, AIn this paper, we extend the Rao-Blackwellised particle filtering method t o more complex hybrid models consisting of Gaussian latent variables and discrete observations. This is accomplished by augmenting the models with artificial variables that enable us to apply Rao-Blackwellisation. Other improvements include the design of an optimal importance proposal distribution and being able to swap the sampling an selection steps to handle out liers. We focus on sequent ial binary classifiers t hat consist of linear- combinations of basis functions, whose coefficients evolve according t o a Gaussian smoothness prior. Our results show significant improvements. |
| spellingShingle | Andrieu, C De Freitas, N Doucet, A Rao-blackwellised particle filtering via data augmentation |
| title | Rao-blackwellised particle filtering via data augmentation |
| title_full | Rao-blackwellised particle filtering via data augmentation |
| title_fullStr | Rao-blackwellised particle filtering via data augmentation |
| title_full_unstemmed | Rao-blackwellised particle filtering via data augmentation |
| title_short | Rao-blackwellised particle filtering via data augmentation |
| title_sort | rao blackwellised particle filtering via data augmentation |
| work_keys_str_mv | AT andrieuc raoblackwellisedparticlefilteringviadataaugmentation AT defreitasn raoblackwellisedparticlefilteringviadataaugmentation AT douceta raoblackwellisedparticlefilteringviadataaugmentation |