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Bayesian multiple testing for dependent data and hidden Markov models - lecture 2
Multi angle
Authors : Gassiat, Elisabeth (Author of the conference)
CIRM (Publisher )
62G07 - Density estimation
62G10 - Nonparametric hypothesis testing
62M99 - None of the above but in this section
CIRM (Publisher )
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Abstract :
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
MSC Codes : 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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Information on the Video
Film maker : Hennenfent, GuillaumeLanguage : English Available date : 15/11/2021 Conference Date : 28/10/2021 Subseries : Research School arXiv category : Statistics Theory ; Machine Learning Mathematical Area(s) : Probability & Statistics Format : MP4 (.mp4) - HD Video Time : 01:18:48 Targeted Audience : Researchers Download : https://videos.cirm-math.fr/2021-10-28_Gassiat_2.mp4 
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Information on the Event
Event Title : End-to-end Bayesian Learning Methods / Solutions de bout-en-bout en apprentissage BayésienEvent Organizers : Cleynen, Alice ; Gloaguen, Pierre ; Le Corff, Sylvain ; Mira, Antonietta ; Stoehr, Julien Dates : 25/10/2021 - 29/10/2021 Event Year : 2021 Event URL : https://conferences.cirm-math.fr/2417.html
Citation Data
DOI : 10.24350/CIRM.V.19825203Cite this video as: Gassiat, Elisabeth (2021). Bayesian multiple testing for dependent data and hidden Markov models - lecture 2. CIRM. Audiovisual resource. doi:10.24350/CIRM.V.19825203 URI : http://dx.doi.org/10.24350/CIRM.V.19825203 |
See Also
- [Multi angle] A tutorial on Bayesian machine learning: what, why and how - lecture 2 / Author of the conference Bardenet, Rémi.
- [Multi angle] Bayesian multiple testing for dependent data and hidden Markov models - lecture 1 / Author of the conference Gassiat, Elisabeth.
- [Multi angle] A tutorial on Bayesian machine learning: what, why and how - lecture 1 / Author of the conference Bardenet, Rémi.
Bibliography
- 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
