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Models for sequence data /

This video gives an overview of Hidden Markov Models and places them in the context of today's popular data modeling methods. First, the assumptions underlying the hidden Markov model (HMM) are explained, illustrating their usage for prediction and filtering of sequential data. The lecturer pro...

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Bibliographic Details
Other Authors: Huang, Bert (Speaker)
Format: Video
Language:English
Language Notes:In English.
Published: Dordrecht, South Holland : Springer Nature, 2022.
Series:Academic Video Online
Subjects:
Hidden Markov models.
Markov processes.
Stochastic processes.
Modèles de Markov cachés.
Processus de Markov.
Processus stochastiques.
Hidden Markov models
Markov processes
Stochastic processes
Educational films
Instructional films
Educational films.
Instructional films.
Films éducatifs.
Films de formation.
Online Access:Connect to this streaming video (Alexander Street Press)
Description
Summary:This video gives an overview of Hidden Markov Models and places them in the context of today's popular data modeling methods. First, the assumptions underlying the hidden Markov model (HMM) are explained, illustrating their usage for prediction and filtering of sequential data. The lecturer provides context when HMMs are useful in comparison to other related approaches such as recurrent neural networks. Finally, an overview of the basic algorithms for performing inference with HMMs and learning their parameters are provided. Watch this video to learn how to model noisy sequences using a classical but highly effective modeling approach.
Item Description:Title from resource description page (viewed January 31, 2023).
Physical Description:1 online resource (41 minutes)
Playing Time:00:40:12