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UID:689@lincs.fr
DTSTART;TZID=Europe/Paris:20220202T110000
DTEND;TZID=Europe/Paris:20220202T120000
DTSTAMP:20220217T134630Z
URL:https://www.lincs.fr/events/infinite-hidden-markov-models/
SUMMARY:Infinite Hidden Markov Models
DESCRIPTION:The Hidden Markov Model is a generative model in which the
 distribution of the observations depends on the state of a Markov chain.
 These models have been successfully applied to many problems\, including
 part-of-speech tagging\, handwriting recognition and time series
 clustering. A common issue is to infer the parameters of the HMM\, given a
 set of observations\, when the number of latent states is unknown. A
 traditional approach is to evaluate models of different dimensions and
 choose the one that offers a good compromise between the complexity of the
 model\, and its adequacy to the data\, in order to prevent
 over-fitting.\n\nIn this talk we will present the Infinite Hidden Markov
 Model\, or HDP-HMM (Hierarchical Dirichlet Process Hidden Markov Model)\,
 which offers an alternative solution to this problem. The HDP-HMM
 generalizes the HMM model by considering the number of state itself as an
 unknown quantity to be inferred\, through Bayesian inference. We will
 discuss the HDP-HMM and its variants\, as well as an application to network
 delay time series clustering.\n\nReferences\n[1] Beal\, M. J.\,
 Ghahramani\, Z.\, &amp\; Rasmussen\, C. E. (2002). The infinite hidden
 Markov model. Advances in neural information processing systems\, 1\,
 577-584.\nhttps://people.csail.mit.edu/jrennie/trg/papers/beal-ihmm-03.pdf\
 n[2] Fox\, E. B.\, Sudderth\, E. B.\, Jordan\, M. I.\, &amp\; Willsky\, A.
 S. (2011). A sticky HDP-HMM with application to speaker diarization. The
 Annals of Applied Statistics\,
 1020-1056.\nhttp://willsky.lids.mit.edu/publ_pdfs/204_pub_AAS.pdf
CATEGORIES:Network Theory,Working Group,Youtube
LOCATION:Paris-Rennes Room (EIT Digital)\, 23 avenue d'Italie\, 75013
 Paris\, France
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=23 avenue d'Italie\, 75013
 Paris\, France;X-APPLE-RADIUS=100;X-TITLE=Paris-Rennes Room (EIT
 Digital):geo:0,0
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TZID:Europe/Paris
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DTSTART:20211031T020000
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TZOFFSETTO:+0100
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