Approximate Bayesian Computation methods for model choice a machine learning point of view - Part 1 | Vidéo | Carmin.tv

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Approximate Bayesian Computation methods for model choice a machine learning point of view - Part 1

De Jean-Michel Marin

Apparaît dans la collection : Thematic month on statistics - Week 5: Bayesian statistics and algorithms / Mois thématique sur les statistiques - Semaine 5 : Semaine Bayésienne et algorithmes

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.

Informations sur la vidéo

Date de captation 29/02/2016
Date de publication 16/03/2016
Institut CIRM
Licence CC BY NC ND
Langue Anglais
Audience Chercheurs
Réalisateur(s) Guillaume Hennenfent
Format MP4

Données de citation

DOI 10.24350/CIRM.V.18937303
Citer cette vidéo Marin, Jean-Michel (29/02/2016). Approximate Bayesian Computation methods for model choice a machine learning point of view - Part 1. CIRM. Audiovisual resource. DOI: 10.24350/CIRM.V.18937303
URL https://dx.doi.org/10.24350/CIRM.V.18937303

Domaine(s)

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

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