Article

Tempering for Bayesian C&RT

Authors:

Nicos Angelopoulos,

James CussensAuthors Info & Claims

ICML '05: Proceedings of the 22nd international conference on Machine learning

Pages 17 - 24

https://doi.org/10.1145/1102351.1102354

Published: 07 August 2005 Publication History

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Abstract

This paper concerns the experimental assessment of tempering as a technique for improving Bayesian inference for C&RT models. Full Bayesian inference requires the computation of a posterior over all possible trees. Since exact computation is not possible Markov chain Monte Carlo (MCMC) methods are used to produce an approximation. C&RT posteriors have many local modes: tempering aims to prevent the Markov chain getting stuck in these modes. Our results show that a clear improvement is achieved using tempering.

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Cited By

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Nanfack GTemple PFrénay B(2022)Constraint Enforcement on Decision Trees: A SurveyACM Computing Surveys10.1145/350673454:10s(1-36)Online publication date: 6-Jan-2022
https://dl.acm.org/doi/10.1145/3506734
Angelopoulos NCussens J(2018)Bayesian learning of Bayesian networks with informative priorsAnnals of Mathematics and Artificial Intelligence10.1007/s10472-009-9133-x54:1-3(53-98)Online publication date: 28-Dec-2018
https://dl.acm.org/doi/10.1007/s10472-009-9133-x
Angelopoulos NCussens J(2017)Distributional logic programming for Bayesian knowledge representationInternational Journal of Approximate Reasoning10.1016/j.ijar.2016.08.00480:C(52-66)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1016/j.ijar.2016.08.004
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Tempering for Bayesian C&RT
1. Mathematics of computing
  1. Probability and statistics

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Published In

ICML '05: Proceedings of the 22nd international conference on Machine learning

August 2005

1113 pages

ISBN:1595931805

DOI:10.1145/1102351

General Chair:
Saso Dzeroski
Jozef Stefan Institute, Slovenia
,
Program Chairs:
Luc De Raedt,
Stefan Wrobel

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 August 2005

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Cited By

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Nanfack GTemple PFrénay B(2022)Constraint Enforcement on Decision Trees: A SurveyACM Computing Surveys10.1145/350673454:10s(1-36)Online publication date: 6-Jan-2022
https://dl.acm.org/doi/10.1145/3506734
Angelopoulos NCussens J(2018)Bayesian learning of Bayesian networks with informative priorsAnnals of Mathematics and Artificial Intelligence10.1007/s10472-009-9133-x54:1-3(53-98)Online publication date: 28-Dec-2018
https://dl.acm.org/doi/10.1007/s10472-009-9133-x
Angelopoulos NCussens J(2017)Distributional logic programming for Bayesian knowledge representationInternational Journal of Approximate Reasoning10.1016/j.ijar.2016.08.00480:C(52-66)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1016/j.ijar.2016.08.004
Angelopoulos NHadjiprocopis AWalkinshaw M(2013)Learning Binding Affinity from Augmented High Throughput Screening DataBioinformatics10.4018/978-1-4666-3604-0.ch020(364-385)Online publication date: 2013
https://doi.org/10.4018/978-1-4666-3604-0.ch020
Angelopoulos NHadjiprocopis AWalkinshaw M(2011)Learning Binding Affinity from Augmented High Throughput Screening DataChemoinformatics and Advanced Machine Learning Perspectives10.4018/978-1-61520-911-8.ch011(212-234)Online publication date: 2011
https://doi.org/10.4018/978-1-61520-911-8.ch011

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Importance tempering

Generalized parallel tempering on Bayesian inverse problems

Likelihood-free parallel tempering