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Markov Chain Monte Carlo Methods - Part 1
Post-edited
Authors : Robert, Christian P. (Author of the conference)
CIRM (Publisher )
60J10 - Markov chains (discrete-time Markov processes on discrete state spaces)
62F15 - Bayesian inference
65C05 - Monte Carlo methods
65C40 - Computational Markov chains (numerical analysis)
CIRM (Publisher )
Abstract :
In this short course, we recall the basics of Markov chain Monte Carlo (Gibbs & Metropolis sampelrs) along with the most recent developments like Hamiltonian Monte Carlo, Rao-Blackwellisation, divide & conquer strategies, pseudo-marginal and other noisy versions. We also cover the specific approximate method of ABC that is currently used in many fields to handle complex models in manageable conditions, from the original motivation in population genetics to the several reinterpretations of the approach found in the recent literature. Time allowing, we will also comment on the programming developments like BUGS, STAN and Anglican that stemmed from those specific algorithms.
MSC Codes : 60J10 - Markov chains (discrete-time Markov processes on discrete state spaces)
62F15 - Bayesian inference
65C05 - Monte Carlo methods
65C40 - Computational Markov chains (numerical analysis)
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Information on the Video
Film maker : Hennenfent, GuillaumeLanguage : English Available date : 16/03/16 Conference Date : 29/02/16 Subseries : Research talks arXiv category : Statistics Theory ; Computer Science Mathematical Area(s) : Probability & Statistics Format : MP4 (.mp4) - HD Video Time : 01:05:32 Targeted Audience : Researchers Download : https://videos.cirm-math.fr/2016-02-29_Robert.mp4 
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Information on the Event
Event Title : Thematic month on statistics - Week 5: Bayesian statistics and algorithms / Mois thématique sur les statistiques - Semaine 5 : Semaine Bayésienne et algorithmesEvent Organizers : Le Gouic, Thibaut ; Pommeret, Denys ; Willer, Thomas Dates : 29/02/2016 - 04/03/16 Event Year : 2016 Event URL : http://conferences.cirm-math.fr/1619.html
Citation Data
DOI : 10.24350/CIRM.V.18936903Cite this video as: Robert, Christian P. (2016). Markov Chain Monte Carlo Methods - Part 1. CIRM. Audiovisual resource. doi:10.24350/CIRM.V.18936903 URI : http://dx.doi.org/10.24350/CIRM.V.18936903 |
See Also
- [Multi angle] Variational Bayes methods and algorithms - Part 1 / Author of the conference Keribin, Christine.
- [Multi angle] The expectation-propagation algorithm: a tutorial - Part 1 / Author of the conference Barthelmé, Simon.
- [Multi angle] Exploring the presence of complex dependence structures in epidemiological and genomic data through flexible clustering / Author of the conference Richardson, Sylvia.
- [Multi angle] Approximate Bayesian Computation methods for model choice a machine learning point of view - Part 1 / Author of the conference Marin, Jean-Michel.
Bibliography
- Robert, C.P., & Casella, G. (2010). Introducing Monte Carlo methods with R. New York : Springer - http://dx.doi.org/10.1007/978-1-4419-1576-4
- Robert, C.P., & Casella, G. (2004). Monte Carlo statistical methods. New York : Springer - http://dx.doi.org/10.1007/978-1-4757-4145-2
