Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • 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
Deriving Probability Density Functions from Probabilistic Functional Programs
Georgia Institute of Technology. (College of Computing)
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science. (Mobility)
Microsoft Research, Cambridge.
Microsoft Research, Cambridge.
2013 (English)In: Tools and Algorithms for the Construction and Analysis of Systems: 19th International Conference, TACAS 2013, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2013, Rome, Italy, March 16-24, 2013. Proceedings / [ed] N. Piterman and S. Smolka, Berlin/Heidelberg: Springer Berlin/Heidelberg, 2013, 508-522 p.Conference paper, Published paper (Refereed)
Abstract [en]

The probability density function of a probability distribution is a fundamental concept in probability theory and a key ingredient in various widely used machine learning methods. However, the necessary framework for compiling probabilistic functional programs to density functions has only recently been developed. In this work, we present a density compiler for a probabilistic language with discrete and continuous distributions, and discrete observations, and provide a proof of its soundness. The compiler greatly reduces the development effort of domain experts, which we demonstrate by solving inference problems from various scientific applications, such as modelling the global carbon cycle, using a standard Markov chain Monte Carlo framework. 

Place, publisher, year, edition, pages
Berlin/Heidelberg: Springer Berlin/Heidelberg, 2013. 508-522 p.
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 7795
Keyword [en]
Logics and Meanings of Programs, Programming Languages, Compilers, Probabilistic Programming
National Category
Computer Science Probability Theory and Statistics
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:uu:diva-197300DOI: 10.1007/978-3-642-36742-7_35ISBN: 978-3-642-36741-0 (print)OAI: oai:DiVA.org:uu-197300DiVA: diva2:618614
Conference
19th International Conference, TACAS 2013, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2013, Rome, Italy, March 16-24, 2013.
Available from: 2013-04-29 Created: 2013-03-21 Last updated: 2014-06-18Bibliographically approved

Open Access in DiVA

DensityFunctions.TACAS13(261 kB)210 downloads
File information
File name FULLTEXT01.pdfFile size 261 kBChecksum SHA-512
646185597ac6dc0f12421f1720b29e3aeafaaef1e8e3f2cb569ca38846d740910ce25df26d93f2ef7dcdc8a4f567edb1394bad19de9838254abb0a47399459b8
Type fulltextMimetype application/pdf

Other links

Publisher's full text

Search in DiVA

By author/editor
Borgström, Johannes
By organisation
Computing Science
Computer ScienceProbability Theory and Statistics

Search outside of DiVA

GoogleGoogle Scholar
Total: 210 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 557 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • 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