Online Bayesian inference for the parameters of PRISM programs.
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| Title: | Online Bayesian inference for the parameters of PRISM programs. |
|---|---|
| Authors: | Cussens, James1 james.cussens@york.ac.uk |
| Source: | Machine Learning. Dec2012, Vol. 89 Issue 3, p279-297. 19p. |
| Subjects: | Bayesian analysis, Logic programming languages, Logic programming, Dirichlet forms, Markov processes, Herbrand's theorem (Number theory) |
| Abstract: | This paper presents a method for approximating posterior distributions over the parameters of a given PRISM program. A sequential approach is taken where the distribution is updated one datapoint at a time. This makes it applicable to online learning situations where data arrives over time. The method is applicable whenever the prior is a mixture of products of Dirichlet distributions. In this case the true posterior will be a mixture of very many such products. An approximation is effected by merging products of Dirichlet distributions. An analysis of the quality of the approximation is presented. Due to the heavy computational burden of this approach, the method has been implemented in the Mercury logic programming language. Initial results using a hidden Markov model and a probabilistic graph are presented. [ABSTRACT FROM AUTHOR] |
| Copyright of Machine Learning is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Items | – Name: Title Label: Title Group: Ti Data: Online Bayesian inference for the parameters of PRISM programs. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Cussens%2C+James%22">Cussens, James</searchLink><relatesTo>1</relatesTo><i> james.cussens@york.ac.uk</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Machine+Learning%22">Machine Learning</searchLink>. Dec2012, Vol. 89 Issue 3, p279-297. 19p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Bayesian+analysis%22">Bayesian analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Logic+programming+languages%22">Logic programming languages</searchLink><br /><searchLink fieldCode="DE" term="%22Logic+programming%22">Logic programming</searchLink><br /><searchLink fieldCode="DE" term="%22Dirichlet+forms%22">Dirichlet forms</searchLink><br /><searchLink fieldCode="DE" term="%22Markov+processes%22">Markov processes</searchLink><br /><searchLink fieldCode="DE" term="%22Herbrand's+theorem+%28Number+theory%29%22">Herbrand's theorem (Number theory)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This paper presents a method for approximating posterior distributions over the parameters of a given PRISM program. A sequential approach is taken where the distribution is updated one datapoint at a time. This makes it applicable to online learning situations where data arrives over time. The method is applicable whenever the prior is a mixture of products of Dirichlet distributions. In this case the true posterior will be a mixture of very many such products. An approximation is effected by merging products of Dirichlet distributions. An analysis of the quality of the approximation is presented. Due to the heavy computational burden of this approach, the method has been implemented in the Mercury logic programming language. Initial results using a hidden Markov model and a probabilistic graph are presented. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Machine Learning is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10994-012-5305-8 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 19 StartPage: 279 Subjects: – SubjectFull: Bayesian analysis Type: general – SubjectFull: Logic programming languages Type: general – SubjectFull: Logic programming Type: general – SubjectFull: Dirichlet forms Type: general – SubjectFull: Markov processes Type: general – SubjectFull: Herbrand's theorem (Number theory) Type: general Titles: – TitleFull: Online Bayesian inference for the parameters of PRISM programs. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Cussens, James IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2012 Type: published Y: 2012 Identifiers: – Type: issn-print Value: 08856125 Numbering: – Type: volume Value: 89 – Type: issue Value: 3 Titles: – TitleFull: Machine Learning Type: main |
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