Structure learning for CTBN's | Vidéo | Carmin.tv

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Structure learning for CTBN's

De Błażej Miasojedow

Apparaît dans la collection : Mathematical Methods of Modern Statistics 2 / Méthodes mathématiques en statistiques modernes 2

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 .

Informations sur la vidéo

Date de captation 05/06/2020
Date de publication 15/06/2020
Institut CIRM
Licence CC BY NC ND
Langue Anglais
Audience Chercheurs
Réalisateur(s) Guillaume Hennenfent
Format MP4

Données de citation

DOI 10.24350/CIRM.V.19646103
Citer cette vidéo Miasojedow, Błażej (05/06/2020). Structure learning for CTBN's. CIRM. Audiovisual resource. DOI: 10.24350/CIRM.V.19646103
URL https://dx.doi.org/10.24350/CIRM.V.19646103

Domaine(s)

Apprentissage

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

Codes MSC

Document(s)

https://www.cirm-math.fr/RepOrga/2146/Slides/Miasojedow.pdf

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