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

00:00:00 / 00:00:00

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

By Nicolas Chopin

Appears in 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.

Information about the video

Date of recording 30/10/2023
Date of publication 27/11/2023
Institution CIRM
Licence CC BY NC ND
Language English
Audience Researchers, Graduate Students
Director(s) Luca Recanzone
Format MP4

Citation data

DOI 10.24350/CIRM.V.20107303
Cite this video 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

Domain(s)

Computation

Bibliography

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

Last related questions on MathOverflow

You have to connect your Carmin.tv account with mathoverflow to add question

Ask a question on MathOverflow

Copyright Carmin.tv 2024

Give feedback