Bayes in Grenoble

Bayes in Grenoble is a new reading group on Bayesian statistical methods held at Inria, Montbonnot. The purpose of this group is to gather the Grenoble Bayesian community on a monthly basis around noteworthy papers. Those can equally focus on theory, methods, learning, applications, computations, etc, and can be seminal papers as well as recent preprints, as soon as they relate to Bayes.

The reading group is organised by Julyan Arbel and Florence Forbes from Inria Mistis team. Feel free to contact us if you wish to attend/be added to the mailing list and/or give a talk. The group is supported by the Grenoble Alpes Data Institute (drinks, snacks and travel support for some speakers).

Here is an ics calendar for programmed sessions.


1st March 2018, Riccardo Corradin (Milan Biccoca & Trinity College Dublin)

Bayesian nonparametric methods for density estimation and clustering on the phase-space. At 14:00 in Room 106, IMAG building (700 Avenue Centrale, Saint-Martin-d'Hères).

8 February 2018, Éric Marchand (Université de Sherbrooke, in sabbatical at UGA)

Estimation with predictive densities: recent results. At 14:00 in Room 106, IMAG building (700 Avenue Centrale, Saint-Martin-d'Hères).

16 January 2018, Łukasz Rajkowski (University of Warsaw)

Analysis of Mode A Posteriori in the Chinese Restaurant Process model, at 11am, in F107, Inria Montbonnot. Buffet at noon, in A109.

Łukasz will present his paper available on arXiv.

11 December 2017, Hongliang Lü (Inria)

Nonparametric Bayesian Image Segmentation, at 11:30, in F107 at Inria Montbonnot.

Hongliang will present the paper Nonparametric Bayesian Image Segmentation. P Orbanz and JM Buhmann. International Journal of Computer Vision (IJCV), Vol. 77, 25-45, 2008. The paper and the associated (matlab) code can be found here: [PDF] [Journal] [Code]

15 November 2017, Julyan Arbel (Inria)

Approximate Bayesian computation (ABC), at 10am, in F107 at Inria Montbonnot.

In this session, we will cover two papers (one seminal, one preprint):

- Fearnhead, P., & Prangle, D. (2012). Constructing summary statistics for approximate Bayesian computation: semi‐automatic approximate Bayesian computation. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 74(3), 419-474. link to paper, link to presentation by the authors.

- Bernton, E., Jacob, P. E., Gerber, M., & Robert, C. P. (2017). Inference in generative models using the Wasserstein distance. arXiv preprint arXiv:1701.05146. link to paper, link to presentation by Xian.

Practical information: location & calendar