Studer, L. ; Paulevé, L. ; Zechner, C. ; Reumann, M. ; Rodriguez Martinez, M. ; Koeppl, H. (2016)
Marginalized Continuous Time Bayesian Networks for Network Reconstruction from Incomplete Observations.
AAAI, Association for the Advancement of Artificial Intelligence. Phoenix, USA (12.-17.02.2016)
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Continuous Time Bayesian Networks (CTBNs) provide a powerful means to model complex network dynamics. However, their inference is computationally demanding—especially if one considers incomplete and noisy time-series data. The latter gives rise to a joint stateand parameter estimation problem, which can only be solved numerically. However, finding the exact parameterization of the CTBN has only secondary importance in most practical scenarios. We therefore propose a method that circumvents the inference of parameters by analytically marginalizing the Markov chain underlying the CTBN model. Since the resulting stochastic process does no longer depend on any model parameters, its inference reduces to a plain nonlinear filtering problem. We solve the latter using a highly efficient implementation of a sequential Monte Carlo scheme. Our framework enables CTBN learning to be applied to incomplete noisy time-series data such as frequently found in molecular biology and other disciplines.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2016 |
| Autor(en): | Studer, L. ; Paulevé, L. ; Zechner, C. ; Reumann, M. ; Rodriguez Martinez, M. ; Koeppl, H. |
| Art des Eintrags: | Bibliographie |
| Titel: | Marginalized Continuous Time Bayesian Networks for Network Reconstruction from Incomplete Observations |
| Sprache: | Englisch |
| Publikationsjahr: | 12 Februar 2016 |
| Ort: | Phoenix, USA |
| Veranstaltungstitel: | AAAI, Association for the Advancement of Artificial Intelligence |
| Veranstaltungsort: | Phoenix, USA |
| Veranstaltungsdatum: | 12.-17.02.2016 |
| URL / URN: | http://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/123... |
| Kurzbeschreibung (Abstract): | Continuous Time Bayesian Networks (CTBNs) provide a powerful means to model complex network dynamics. However, their inference is computationally demanding—especially if one considers incomplete and noisy time-series data. The latter gives rise to a joint stateand parameter estimation problem, which can only be solved numerically. However, finding the exact parameterization of the CTBN has only secondary importance in most practical scenarios. We therefore propose a method that circumvents the inference of parameters by analytically marginalizing the Markov chain underlying the CTBN model. Since the resulting stochastic process does no longer depend on any model parameters, its inference reduces to a plain nonlinear filtering problem. We solve the latter using a highly efficient implementation of a sequential Monte Carlo scheme. Our framework enables CTBN learning to be applied to incomplete noisy time-series data such as frequently found in molecular biology and other disciplines. |
| Freie Schlagworte: | sequential Monte Carlo; graph reconstruction; continuous time Bayesian network |
| Zusätzliche Informationen: | Copyright 2015 Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. |
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik > Bioinspirierte Kommunikationssysteme 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik |
| Hinterlegungsdatum: | 08 Dez 2015 10:59 |
| Letzte Änderung: | 23 Sep 2021 14:31 |
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