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Approximate Bayesian Computation methods for model choice a machine learning point of view - Part 1
Multi angle
Authors : Marin, Jean-Michel (Author of the conference)
CIRM (Publisher )
62F15 - Bayesian inference
65C60 - Computational problems in statistics
CIRM (Publisher )
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Abstract :
Approximate Bayesian computation (ABC) techniques, also known as likelihood-free methods, have become a standard tool for the analysis of complex models, primarily in population genetics. The development of new ABC methodologies is undergoing a rapid increase in the past years, as shown by multiple publications, conferences and softwares. In this lecture, we introduce some recent advances on ABC techniques, notably for model choice problems.
Keywords : likelihood-free methods; Bayesian statistics; ABC methodology; DIYABC; Bayesian model choice; Gibbs random fields
MSC Codes : Keywords : likelihood-free methods; Bayesian statistics; ABC methodology; DIYABC; Bayesian model choice; Gibbs random fields
62F15 - Bayesian inference
65C60 - Computational problems in statistics
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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 : Machine Learning ; Computation Mathematical Area(s) : Probability & Statistics Format : MP4 (.mp4) - HD Video Time : 01:01:46 Targeted Audience : Researchers Download : https://videos.cirm-math.fr/2016-02-29_Marin.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 : 29/02/16 - 04/03/16 Event Year : 2016 Event URL : http://conferences.cirm-math.fr/1619.html
Citation Data
DOI : 10.24350/CIRM.V.18937303Cite this video as: Marin, Jean-Michel (2016). Approximate Bayesian Computation methods for model choice a machine learning point of view - Part 1. CIRM. Audiovisual resource. doi:10.24350/CIRM.V.18937303 URI : http://dx.doi.org/10.24350/CIRM.V.18937303 |
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.
- [Post-edited] Markov Chain Monte Carlo Methods - Part 1 / Author of the conference Robert, Christian P..
Bibliography
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- Marin, J.-M., & Robert, C.P. (2014). Bayesian essentials with R. New York : Springer - http://dx.doi.org/10.1007/978-1-4614-8687-9
- Nielsen, R., & Slatkin, M. (2013). An introduction to population genetics : theory and applications. Sunderland : Sinauer Associates - http://www.sinauer.com/an-introduction-to-population-genetics-theory-and-applications.html
- Robert, C.P. (2001). The Bayesian choice. From decision-theoretic foundations to computational implementation. New York : Springer - http://dx.doi.org/10.1007/0-387-71599-1
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