Bayesian nonparametric statistics
The course aims at providing a modern overview of Bayesian nonparametric statistics. Students will learn how to model statistical and machine learning problems with Bayesian nonparametric tools and study theoretical properties of the involved objects.
This course covers the fundamentals of Bayesian nonparametric inference and focuses on the key probabilistic concepts and stochastic modelling tools at the basis of the most recent advances in the field.
• Foundations of Bayesian nonparametric inference: exchangeability, de Finetti's representation theorem
• Clustering and Dirichlet process, random partitions
• Models beyond the Dirichlet process, random measures
• Latent features and the Indian buffet process
• Some other popular models: Mixture models, Hierarchical models, Covariate-dependent models, Network models
• Discovery probabilities: comparing BNP & Good-Turing estimators
• Posterior distributions and some elements of Bayesian asymptotics
Lecture notes (handwritten) essentially based on the following bibliography.
- Jayanta Ghosh, and R. V. Ramamoorthi. 2003. Bayesian Nonparametrics. Springer Series in Statistics.
- Nils Hjort, Chris Holmes, Peter Müller, and Stephen Walker. 2010. Bayesian Nonparametrics. Cambridge Series in Statistical and Probabilistic Mathematics. Cambridge University Press.
- Peter Orbanz. 2012. Lecture Notes on Bayesian Nonparametrics. Available at: http://stat.columbia.edu/~porbanz/papers/porbanz_BNP_draft.pdf
- Bas Kleijn, Aad van der Vaart, Harry van Zanten. 2012. Lectures on Nonparametric Bayesian Statistics. Available at: https://staff.fnwi.uva.nl/b.j.k.kleijn/NPBayes-LecNotes-2015.pdf
The course is intended for PhD students of MSTII Ecole doctorale. However, it is open to anyone interested.
On Thursday mornings, 9.00 to 12.00, January 5, 12, 19, 26 and February 2, 2017
Room A103, Inria Grenoble Rhône-Alpes
655 Avenue de l'Europe
How do I register?
Send me an email: firstname.lastname@example.org
Upcoming 11th Conference on Bayesian Nonparametrics. Paris, 26-30 June 2017
DPpackage: Bayesian Semi- and Nonparametric Modeling in R, an R package by Alejandro Jara
BNPdensity, an R package for modeling mixtures with normalized random measures, by Ernesto Barrios, Antonio Lijoi, Luis E. Nieto-Barajas and Igor Prünster