CIRM - Videos & books Library - Structure learning for CTBN's

Virtualconference

Auteurs : Miasojedow, Błażej (Auteur de la Conférence)
CIRM (Editeur )

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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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Hennenfent, Guillaume

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https://videos.cirm-math.fr/2020-06-05_Miasojedow.mp4

Informations sur la Rencontre

Nom de la rencontre : Mathematical Methods of Modern Statistics 2 / Méthodes mathématiques en statistiques modernes 2
Organisateurs 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-...

Données de citation

DOI : 10.24350/CIRM.V.19646103
Citer cette vidéo: 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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Bibliographie

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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