EnglishApproximate Bayesian Computation methods for model choice a machine learning point of view - Part 1
Multi angle
Auteurs : Marin, Jean-Michel (Auteur de la Conférence)
CIRM (Editeur )
62F15 - Bayesian inference
65C60 - Computational problems in statistics
CIRM (Editeur )
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Résumé :
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
Codes MSC : 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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Informations sur la Vidéo
Langue : AnglaisDate de publication : 16/03/16 Date de captation : 29/02/16 Sous collection : Research talks arXiv category : Machine Learning ; Computation Domaine : Probability & Statistics Format : MP4 (.mp4) - HD Durée : 01:01:46 Audience : Researchers Download : https://videos.cirm-math.fr/2016-02-29_Marin.mp4 
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Informations sur la Rencontre
Nom de la rencontre : Thematic month on statistics - Week 5: Bayesian statistics and algorithms / Mois thématique sur les statistiques - Semaine 5 : Semaine Bayésienne et algorithmesOrganisateurs de la rencontre : Le Gouic, Thibaut ; Pommeret, Denys ; Willer, Thomas Dates : 29/02/16 - 04/03/16 Année de la rencontre : 2016 URL Congrès : http://conferences.cirm-math.fr/1619.html
Données de citation
DOI : 10.24350/CIRM.V.18937303Citer cette vidéo: 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 |
Bibliographie
- Ewens, W.J. (200). Mathematical population genetics. I : theoretical introduction. New York : Springer - http://dx.doi.org/10.1007/978-0-387-21822-9
- Gelman, A., Carlin, J.B., Stern, H.S., Dunson, D.B., Vehtari, A., & Rubin, D.B. (Eds.). (2014). Bayesian data analysis. Boca Raton : CRC Press - https://www.crcpress.com/Bayesian-Data-Analysis-Third-Edition/Gelman-Carlin-Stern-Dunson-Vehtari-Rubin/9781439840955
- 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
- Wakeley, J. (2009). Coalescent theory : an introduction. Greenwood Village : Roberts & Co. Publishers - http://www.roberts-publishers.com/authors/wakeley-john/coalescent-theory.html
