An introduction to state-space models, particle filters, and Sequential Monte Carlo samplers - Part 1 | Vidéo | Carmin.tv

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An introduction to state-space models, particle filters, and Sequential Monte Carlo samplers - Part 1

De Nicolas Chopin

Apparaît dans la collection : Autumn school in Bayesian Statistics / École d'automne en statistique bayésienne

This course will provide a general introduction to SMC algorithms, from basic particle filters and their uses in state-space (hidden Markov) modelling in various areas, to more advanced algorithms such as SMC samplers, which may be used to sample from one, or several target distributions. The course will cover “a bit of everything”: theory (using Feynman-Kac models as a general framework), methodology (how to construct better algorithms in practice), implementation (examples in Python based on the library particles will be showcased), and applications.

Informations sur la vidéo

Date de captation 30/10/2023
Date de publication 27/11/2023
Institut CIRM
Licence CC BY NC ND
Langue Anglais
Audience Chercheurs, Doctorants
Réalisateur(s) Luca Recanzone
Format MP4

Données de citation

DOI 10.24350/CIRM.V.20107303
Citer cette vidéo Chopin, Nicolas (30/10/2023). An introduction to state-space models, particle filters, and Sequential Monte Carlo samplers - Part 1. CIRM. Audiovisual resource. DOI: 10.24350/CIRM.V.20107303
URL https://dx.doi.org/10.24350/CIRM.V.20107303

Domaine(s)

Calcul

Bibliographie

CHOPIN, Nicolas, PAPASPILIOPOULOS, Omiros, et al. An introduction to sequential Monte Carlo. New York : Springer, 2020. - https://doi.org/10.1007/978-3-030-47845-2

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