uu.seUppsala University Publications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Delayed sampling and automatic Rao-Blackwellization of probabilistic programs
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
Show others and affiliations
2018 (English)In: Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS), Lanzarote, Spain, April, 2018, 2018Conference paper, Published paper (Refereed)
Abstract [en]

We introduce a dynamic mechanism for the solution of analytically-tractable substructure in probabilistic programs, using conjugate priors and affine transformations to reduce variance in Monte Carlo estimators. For inference with Sequential Monte Carlo, this automatically yields improvements such as locallyoptimal proposals and Rao–Blackwellization. The mechanism maintains a directed graph alongside the running program that evolves dynamically as operations are triggered upon it. Nodes of the graph represent random variables, edges the analytically-tractable relationships between them. Random variables remain in the graph for as long as possible, to be sampled only when they are used by the program in a way that cannot be resolved analytically. In the meantime, they are conditioned on as many observations as possible. We demonstrate the mechanism with a few pedagogical examples, as well as a linearnonlinear state-space model with simulated data, and an epidemiological model with real data of a dengue outbreak in Micronesia. In all cases one or more variables are automatically marginalized out to significantly reduce variance in estimates of the marginal likelihood, in the final case facilitating a randomweight or pseudo-marginal-type importance sampler for parameter estimation. We have implemented the approach in Anglican and a new probabilistic programming language called Birch.

Place, publisher, year, edition, pages
2018.
National Category
Probability Theory and Statistics Computer Sciences
Identifiers
URN: urn:nbn:se:uu:diva-368626OAI: oai:DiVA.org:uu-368626DiVA, id: diva2:1268456
Conference
21st International Conference on Artificial Intelligence and Statistics (AISTATS), april, 2018
Funder
Swedish Foundation for Strategic Research , RIT15-0012Swedish Research Council, 2013-4853Available from: 2018-12-05 Created: 2018-12-05 Last updated: 2019-03-06Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

http://proceedings.mlr.press/v84/murray18a/murray18a.pdf

Authority records BETA

Murray, LawrenceSchön, Thomas B.

Search in DiVA

By author/editor
Murray, LawrenceSchön, Thomas B.
By organisation
Division of Systems and ControlAutomatic controlComputing Science
Probability Theory and StatisticsComputer Sciences

Search outside of DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric score

urn-nbn
Total: 105 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf