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
Elements of Sequential Monte Carlo
Columbia Univ, New York, NY 10027 USA.
Linkoping Univ, Linkoping, Sweden.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.ORCID iD: 0000-0001-5183-234X
2019 (English)In: FOUNDATIONS AND TRENDS IN MACHINE LEARNING, ISSN 1935-8237, Vol. 12, no 3, p. 187-306Article in journal (Refereed) Published
Abstract [en]

A core problem in statistics and probabilistic machine learning is to compute probability distributions and expectations. This is the fundamental problem of Bayesian statistics and machine learning, which frames all inference as expectations with respect to the posterior distribution. The key challenge is to approximate these intractable expectations. In this tutorial, we review sequential Monte Carlo (SMC), a random-sampling-based class of methods for approximate inference. First, we explain the basics of SMC, discuss practical issues, and review theoretical results. We then examine two of the main user design choices: the proposal distributions and the so called intermediate target distributions. We review recent results on how variational inference and amortization can be used to learn efficient proposals and target distributions. Next, we discuss the SMC estimate of the normalizing constant, how this can be used for pseudo-marginal inference and inference evaluation. Throughout the tutorial we illustrate the use of SMC on various models commonly used in machine learning, such as stochastic recurrent neural networks, probabilistic graphical models, and probabilistic programs.

Place, publisher, year, edition, pages
NOW PUBLISHERS INC , 2019. Vol. 12, no 3, p. 187-306
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:uu:diva-400416DOI: 10.1561/2200000074ISI: 000500235400001OAI: oai:DiVA.org:uu-400416DiVA, id: diva2:1381421
Available from: 2019-12-20 Created: 2019-12-20 Last updated: 2019-12-20Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full text

Authority records BETA

Schön, Thomas B.

Search in DiVA

By author/editor
Schön, Thomas B.
By organisation
Division of Systems and Control
Probability Theory and Statistics

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 1 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