End-to-end Bayesian Learning Methods / Solutions de bout-en-bout en apprentissage Bayésien

Collection End-to-end Bayesian Learning Methods / Solutions de bout-en-bout en apprentissage Bayésien

Organizer(s) Cleynen, Alice ; Gloaguen, Pierre ; Le Corff, Sylvain ; Mira, Antonietta ; Stoehr, Julien
Date(s) 25/10/2021 - 29/10/2021
linked URL https://conferences.cirm-math.fr/2417.html
00:00:00 / 00:00:00
1 4

Bayesian multiple testing for dependent data and hidden Markov models - lecture 1

By Elisabeth Gassiat

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.

Information about the video

Citation data

  • DOI 10.24350/CIRM.V.19825003
  • Cite this video Gassiat, Elisabeth (26/10/2021). Bayesian multiple testing for dependent data and hidden Markov models - lecture 1. CIRM. Audiovisual resource. DOI: 10.24350/CIRM.V.19825003
  • URL https://dx.doi.org/10.24350/CIRM.V.19825003

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

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