Back to Search

Conference item

Output-sensitive Adaptive Metropolis-Hastings for probabilistic programs

Abstract:: We introduce an adaptive output-sensitive Metropolis-Hastings algorithm for probabilistic models expressed as programs, Adaptive Lightweight Metropolis-Hastings (AdLMH). This algorithm extends Lightweight Metropolis-Hastings (LMH) by adjusting the probabilities of proposing random variables for modification to improve convergence of the program output. We show that AdLMH converges to the correct equilibrium distribution and compare convergence of AdLMH to that of LMH on several test problems ...
Expand abstract
Collapse abstract

Publication status:: Published

Peer review status:: Peer reviewed

Actions

Email

Email this record

Send the bibliographic details of this record to your email address.

Your Email
Please enter the email address that the record information will be sent to.

-
Your message (optional)
Please add any additional information to be included within the email.
Cite

Cite this record

APA Style

Tolpin, D., de Meent, J., Paige, B., & Wood, F. (2015). Output-sensitive Adaptive Metropolis-Hastings for probabilistic programs. Lecture Notes in Computer Science: ECML PKDD 2015: Machine Learning and Knowledge Discovery in Databases, 9285, 311–326.

MLA Style

Tolpin, D., et al. “Output-Sensitive Adaptive Metropolis-Hastings for Probabilistic Programs.” Lecture Notes in Computer Science: ECML PKDD 2015: Machine Learning and Knowledge Discovery in Databases, vol. 9285, Springer, 2015, pp. 311–26.

Chicago Style

Tolpin, D, J de Meent, B Paige, and F Wood. 2015. “Output-Sensitive Adaptive Metropolis-Hastings for Probabilistic Programs.” In Lecture Notes in Computer Science: ECML PKDD 2015: Machine Learning and Knowledge Discovery in Databases, 9285:311–26. Springer.
Tweet
Print

Access Document

Files:: Tolpin-ECMLPKDD-2015.pdf

(Accepted manuscript, pdf, 1.1MB)

Publisher copy:: 10.1007/978-3-319-23525-7_19

Authors

+ Tolpin, D More by this author

Role:

Author

+ de Meent, J More by this author

Role:

Author

+ Paige, B More by this author

Role:

Author

+ Wood, F More by this author

Role:

Author

Bibliographic Details

Publisher:: Springer Publisher's website
Host title:: Lecture Notes in Computer Science: ECML PKDD 2015: Machine Learning and Knowledge Discovery in Databases
Journal:: Lecture Notes in Computer Science: ECML PKDD 2015: Machine Learning and Knowledge Discovery in Databases Journal website
Volume:: 9285
Pages:: 311-326
Publication date:: 2015-08-29
DOI:: 10.1007/978-3-319-23525-7_19
ISSN:: 1611-3349 and 0302-9743
ISBN:: 9783319235240

Item Description

Keywords:: Adaptive MCMC

Probabilistic programming

SBTMR
Pubs id:: pubs:579427
UUID:: uuid:0ac75ab0-4217-4da3-9049-54eae28df111
Local pid:: pubs:579427
Source identifiers:: 579427
Deposit date:: 2017-03-23

Terms of use

Copyright holder:: Springer International Publishing Switzerland
Copyright date:: 2015
Notes:: © Springer International Publishing Switzerland 2015. This is the Accepted Manuscript version of the article. The final version is available online from Springer at: https://doi.org/10.1007/978-3-319-23525-7_19

Licence:: Terms and Conditions of Use for Oxford University Research Archive

Metrics

Views and Downloads

About views and downloads

If you are the owner of this record, you can report an update to it here: Report update to this record

TO TOP