Bayesian Optimization for Probabilistic Programs
We outline a general purpose framework for black-box marginal maximum a pos- teriori estimation of probabilistic program variables using Bayesian optimization with Gaussian processes. We introduce the concept of an optimization query, whereby a probabilistic program returns an infinite lazy sequence...
| المؤلفون الرئيسيون: | , , , , |
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| التنسيق: | Conference item |
| منشور في: |
Neural Information Processing Systems Foundation
2016
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| _version_ | 1826297585351000064 |
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| author | Rainforth, T Le, T van de Meent, J Osborne, M Wood, F |
| author_facet | Rainforth, T Le, T van de Meent, J Osborne, M Wood, F |
| author_sort | Rainforth, T |
| collection | OXFORD |
| description | We outline a general purpose framework for black-box marginal maximum a pos- teriori estimation of probabilistic program variables using Bayesian optimization with Gaussian processes. We introduce the concept of an optimization query, whereby a probabilistic program returns an infinite lazy sequence of increasingly optimal estimates, and explain how a general purpose program transformation would allow the evidence of any probabilistic program, and therefore any graphical model, to be optimized with respect to an arbitrary subset of its variables. |
| first_indexed | 2024-03-07T04:33:53Z |
| format | Conference item |
| id | oxford-uuid:cf44b369-52b5-40fc-bc3d-b4e3f69bf86c |
| institution | University of Oxford |
| last_indexed | 2024-03-07T04:33:53Z |
| publishDate | 2016 |
| publisher | Neural Information Processing Systems Foundation |
| record_format | dspace |
| spelling | oxford-uuid:cf44b369-52b5-40fc-bc3d-b4e3f69bf86c2022-03-27T07:41:16ZBayesian Optimization for Probabilistic ProgramsConference itemhttp://purl.org/coar/resource_type/c_5794uuid:cf44b369-52b5-40fc-bc3d-b4e3f69bf86cSymplectic Elements at OxfordNeural Information Processing Systems Foundation2016Rainforth, TLe, Tvan de Meent, JOsborne, MWood, FWe outline a general purpose framework for black-box marginal maximum a pos- teriori estimation of probabilistic program variables using Bayesian optimization with Gaussian processes. We introduce the concept of an optimization query, whereby a probabilistic program returns an infinite lazy sequence of increasingly optimal estimates, and explain how a general purpose program transformation would allow the evidence of any probabilistic program, and therefore any graphical model, to be optimized with respect to an arbitrary subset of its variables. |
| spellingShingle | Rainforth, T Le, T van de Meent, J Osborne, M Wood, F Bayesian Optimization for Probabilistic Programs |
| title | Bayesian Optimization for Probabilistic Programs |
| title_full | Bayesian Optimization for Probabilistic Programs |
| title_fullStr | Bayesian Optimization for Probabilistic Programs |
| title_full_unstemmed | Bayesian Optimization for Probabilistic Programs |
| title_short | Bayesian Optimization for Probabilistic Programs |
| title_sort | bayesian optimization for probabilistic programs |
| work_keys_str_mv | AT rainfortht bayesianoptimizationforprobabilisticprograms AT let bayesianoptimizationforprobabilisticprograms AT vandemeentj bayesianoptimizationforprobabilisticprograms AT osbornem bayesianoptimizationforprobabilisticprograms AT woodf bayesianoptimizationforprobabilisticprograms |