English
Bayesian multiple testing for dependent data and hidden Markov models - lecture 1
Multi angle
Auteurs : Gassiat, Elisabeth (Auteur de la Conférence)
CIRM (Editeur )
62G07 - Density estimation
62G10 - Nonparametric hypothesis testing
62M99 - None of the above but in this section
CIRM (Editeur )
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Résumé :
Hidden markov models (HMMs) have the interesting property that they can be used to model mixtures of populations for dependent data without prior parametric assumptions on the populations. HMMs can be used to build flexible priors.
I will present recent results on empirical Bayes multiple testing, non parametric inference of HMMs and fundamental limits in the learning of HMMs.
Keywords : multiple testing; hidden Markov models; non parametric inference; classification
Codes MSC : I will present recent results on empirical Bayes multiple testing, non parametric inference of HMMs and fundamental limits in the learning of HMMs.
Keywords : multiple testing; hidden Markov models; non parametric inference; classification
62G07 - Density estimation
62G10 - Nonparametric hypothesis testing
62M99 - None of the above but in this section
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Informations sur la Vidéo
Réalisateur : Hennenfent, GuillaumeLangue : Anglais Date de publication : 15/11/2021 Date de captation : 26/10/2021 Sous collection : Research School arXiv category : Statistics Theory ; Machine Learning Domaine : Probability & Statistics Format : MP4 (.mp4) - HD Durée : 01:26:19 Audience : Researchers Download : https://videos.cirm-math.fr/2021-10-26_Gassiat_1.mp4 
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Informations sur la Rencontre
Nom de la rencontre : End-to-end Bayesian Learning Methods / Solutions de bout-en-bout en apprentissage BayésienOrganisateurs de la rencontre : Cleynen, Alice ; Gloaguen, Pierre ; Le Corff, Sylvain ; Mira, Antonietta ; Stoehr, Julien Dates : 25/10/2021 - 29/10/2021 Année de la rencontre : 2021 URL Congrès : https://conferences.cirm-math.fr/2417.html
Données de citation
DOI : 10.24350/CIRM.V.19825003Citer cette vidéo: Gassiat, Elisabeth (2021). Bayesian multiple testing for dependent data and hidden Markov models - lecture 1. CIRM. Audiovisual resource. doi:10.24350/CIRM.V.19825003 URI : http://dx.doi.org/10.24350/CIRM.V.19825003 |
Voir aussi
- [Multi angle] Bayesian multiple testing for dependent data and hidden Markov models - lecture 2 / Auteur de la Conférence Gassiat, Elisabeth.
- [Multi angle] A tutorial on Bayesian machine learning: what, why and how - lecture 2 / Auteur de la Conférence Bardenet, Rémi.
- [Multi angle] A tutorial on Bayesian machine learning: what, why and how - lecture 1 / Auteur de la Conférence Bardenet, Rémi.
Bibliographie
- ABRAHAM, Kweku, CASTILLO, Ismael, et GASSIAT, Elisabeth. Multiple testing in nonparametric hidden Markov models: An empirical Bayes approach. arXiv preprint arXiv:2101.03838, 2021. - https://arxiv.org/abs/2101.03838
- ABRAHAM, Kweku, NAULET, Zacharie, et GASSIAT, Elisabeth. Fundamental limits for learning hidden Markov model parameters. arXiv preprint arXiv:2106.12936, 2021. - https://arxiv.org/abs/2106.12936
- DE CASTRO, Yohann, GASSIAT, Élisabeth, et LACOUR, Claire. Minimax adaptive estimation of nonparametric hidden Markov models. The Journal of Machine Learning Research, 2016, vol. 17, no 1, p. 3842-3884. - https://www.jmlr.org/papers/volume17/15-381/15-381.pdf
