Maximum Likelihood Learning of Conditional MTE Distributions

Helge Langseth; Thomas Dyhre Nielsen; Rafael Rumí; Antonio Salmerón

doi:10.1007/978-3-642-02906-6_22

Maximum Likelihood Learning of Conditional MTE Distributions

Helge Langseth, Thomas Dyhre Nielsen, Rafael Rumí, Antonio Salmerón

Institut for Datalogi

Publikation: Bidrag til tidsskrift › Konferenceartikel i tidsskrift › Forskning › peer review

15 Citationer (Scopus)

563 Downloads (Pure)

Abstract

We describe a procedure for inducing conditional densities within the mixtures of truncated exponentials (MTE) framework. We analyse possible conditional MTE speciﬁcations and propose a model selection scheme, based on the BIC score, for partitioning the domain of the conditioning variables. Finally, experimental results demonstrate the
applicability of the learning procedure as well as the expressive power of the conditional MTE distribution.

Originalsprog	Engelsk
Bogserie	Lecture Notes in Computer Science
Vol/bind	5590
Sider (fra-til)	240-251
ISSN	0302-9743
DOI	https://doi.org/10.1007/978-3-642-02906-6_22
Status	Udgivet - 2009
Begivenhed	The European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty - Verona, Italien Varighed: 1 jul. 2009 → 3 jul. 2009 Konferencens nummer: 10

Konference

Konference	The European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Nummer	10
Land/Område	Italien
By	Verona
Periode	01/07/2009 → 03/07/2009

Bibliografisk note

Titel:
Proceedings of the Tenth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty

Oversat titel:

Oversat undertitel:

Forlag:
Springer

ISBN (Trykt):
978-3-642-02905-9

ISBN (Elektronisk):

Publikationsserier:
Lecture Notes in Computer Science, Springer Verlag, 0302-9743, 1611-3349, 5590

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10.1007/978-3-642-02906-6_22

ECSQARU09-LangsethNielsenRumiSalmeronIndsendt manuskript, 297 KB

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Citationsformater

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title = "Maximum Likelihood Learning of Conditional MTE Distributions",

abstract = "We describe a procedure for inducing conditional densities within the mixtures of truncated exponentials (MTE) framework. We analyse possible conditional MTE speciﬁcations and propose a model selection scheme, based on the BIC score, for partitioning the domain of the conditioning variables. Finally, experimental results demonstrate the applicability of the learning procedure as well as the expressive power of the conditional MTE distribution. ",

author = "Helge Langseth and Nielsen, {Thomas Dyhre} and Rafael Rum{\'i} and Antonio Salmer{\'o}n",

note = "Titel: Proceedings of the Tenth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty Oversat titel: Oversat undertitel: Forlag: Springer ISBN (Trykt): 978-3-642-02905-9 ISBN (Elektronisk): Publikationsserier: Lecture Notes in Computer Science, Springer Verlag, 0302-9743, 1611-3349, 5590 ; The European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty ; Conference date: 01-07-2009 Through 03-07-2009",

year = "2009",

doi = "10.1007/978-3-642-02906-6_22",

language = "English",

volume = "5590",

pages = "240--251",

journal = "Lecture Notes in Computer Science",

issn = "0302-9743",

publisher = "Physica-Verlag",

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T1 - Maximum Likelihood Learning of Conditional MTE Distributions

AU - Langseth, Helge

AU - Nielsen, Thomas Dyhre

AU - Rumí, Rafael

AU - Salmerón, Antonio

N1 - Conference code: 10

PY - 2009

Y1 - 2009

N2 - We describe a procedure for inducing conditional densities within the mixtures of truncated exponentials (MTE) framework. We analyse possible conditional MTE speciﬁcations and propose a model selection scheme, based on the BIC score, for partitioning the domain of the conditioning variables. Finally, experimental results demonstrate the applicability of the learning procedure as well as the expressive power of the conditional MTE distribution.

AB - We describe a procedure for inducing conditional densities within the mixtures of truncated exponentials (MTE) framework. We analyse possible conditional MTE speciﬁcations and propose a model selection scheme, based on the BIC score, for partitioning the domain of the conditioning variables. Finally, experimental results demonstrate the applicability of the learning procedure as well as the expressive power of the conditional MTE distribution.

U2 - 10.1007/978-3-642-02906-6_22

DO - 10.1007/978-3-642-02906-6_22

M3 - Conference article in Journal

SN - 0302-9743

VL - 5590

SP - 240

EP - 251

JO - Lecture Notes in Computer Science

JF - Lecture Notes in Computer Science

T2 - The European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty

Y2 - 1 July 2009 through 3 July 2009

ER -

Maximum Likelihood Learning of Conditional MTE Distributions

Abstract

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