Virtualconference
H Structure learning for CTBN's
Auteurs : Miasojedow, Błażej (Auteur de la Conférence)
CIRM (Editeur )
CIRM (Editeur )
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- LEZAUD, Pascal. Chernoff-type bound for finite Markov chains. Annals of Applied Probability, 1998, p. 849-867. - http://dx.doi.org/10.1214/aoap/1028903453
- LINZNER, Dominik et KOEPPL, Heinz. Cluster variational approximations for structure learning of continuous-time Bayesian networks from incomplete data. In : Advances in Neural Information Processing Systems. 2018. p. 7880-7890. - http://papers.nips.cc/paper/8013-cluster-variational-approximations-for-structure-learning-of-continuous-time-bayesian-networks-from-incomplete-data.pdf
- LINZNER, Dominik, SCHMIDT, Michael, et KOEPPL, Heinz. Scalable Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data. In : Advances in Neural Information Processing Systems. 2019. p. 3741-3751. - https://arxiv.org/abs/1909.04570
- NODELMAN, Uri. Continuous Time Bayesian Networks. PhD thesis, Department of Computer Science,
Stanford University, 2007. - https://ai.stanford.edu/~nodelman/papers/ctbn-thesis.pdf
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Résumé :
The continuous time Bayesian networks (CTBNs) represent a class of stochastic processes, which can be used to model complex phenomena, for instance, they can describe interactions occurring in living processes, in social science models or in medicine. The literature on this topic is usually focused on the case, when the dependence structure of a system is known and we are to determine conditional transition intensities (parameters of the network). In the paper, we study the structure learning problem, which is a more challenging task and the existing research on this topic is limited. The approach, which we propose, is based on a penalized likelihood method. We prove that our algorithm, under mild regularity conditions, recognizes the dependence structure of the graph with high probability. We also investigate the properties of the procedure in numerical studies to demonstrate its effectiveness .
Keywords : Bayesian networks; continuous time Bayesian networks; continuous time Markov processes; Lasso penalty; model selection
Codes MSC :
60J27
- Continuous-time Markov processes on discrete state spaces
62F30
- Inference under constraints
62M05
- Markov processes: estimation
Ressources complémentaires :
https://www.cirm-math.fr/RepOrga/2146/Slides/Miasojedow.pdf
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Informations sur la Vidéo
Réalisateur : Hennenfent, GuillaumeLangue : Anglais Date de publication : 15/06/2020 Date de captation : 05/06/2020 Collection : Research talks ; Probability and Statistics Durée : 00:42:35 Domaine : Probability & Statistics Audience : Chercheurs ; Doctorants , Post - Doctorants Download : https://videos.cirm-math.fr/2020-06-05_Miasojedow.mp4 
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Informations sur la Rencontre Virtuelle
Nom de la rencontre : Mathematical Methods of Modern Statistics 2 / Méthodes mathématiques en statistiques modernes 2Organisateurs de la rencontre : Bogdan, Malgorzata ; Graczyk, Piotr ; Panloup, Fabien ; Proïa, Frédéric ; Roquain, Etienne Dates : 15/06/2020 - 19/06/2020 Année de la rencontre : 2020 URL Congrès : https://www.cirm-math.com/cirm-virtual-...
Citation Data
DOI : 10.24350/CIRM.V.19646103Cite this video as: Miasojedow, Błażej (2020). Structure learning for CTBN's.CIRM . Audiovisual resource. doi:10.24350/CIRM.V.19646103 URI : http://dx.doi.org/10.24350/CIRM.V.19646103 |
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