Parallel importance sampling in conditional linear Gaussian networks

Publikation: Forskning - peer reviewKonferenceartikel i proceeding

Abstrakt

In this paper we analyse the problem of probabilistic inference in CLG networks when evidence comes in streams. In such situations, fast and scalable algorithms, able to provide accurate responses in a short time are required. We consider the instantiation of variational inference and importance sampling, two well known tools for probabilistic inference, to the CLG case. The experimental results over synthetic networks show how a parallel version importance sampling, and more precisely evidence weighting, is a promising scheme, as it is accurate and scales up with respect to available computing resources.
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Detaljer

In this paper we analyse the problem of probabilistic inference in CLG networks when evidence comes in streams. In such situations, fast and scalable algorithms, able to provide accurate responses in a short time are required. We consider the instantiation of variational inference and importance sampling, two well known tools for probabilistic inference, to the CLG case. The experimental results over synthetic networks show how a parallel version importance sampling, and more precisely evidence weighting, is a promising scheme, as it is accurate and scales up with respect to available computing resources.
OriginalsprogEngelsk
TitelAdvances in Artificial Intelligence : 16th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2015 Albacete, Spain, November 9–12, 2015 Proceedings
RedaktørerJosé M. Puerta, José A. Gámez, Bernabe Dorronsoro, Edurne Barrenechea, Alicia Troncoso, Bruno Baruque, Mikel Galar
UdgiverSpringer
Publikationsdato2015
Sider36-46
ISBN (trykt)978-3-319-24597-3
ISBN (elektronisk)978-3-319-24598-0
DOI
StatusUdgivet - 2015
Begivenhed - Albacete, Spanien

Konference

KonferenceConference of the Spanish Association for Artificial Intelligence, CAEPIA 2015
Nummer16th
LandSpanien
ByAlbacete
Periode09/11/201512/11/2015
SerieLecture Notes in Computer Science
Nummer9422
ISSN0302-9743

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