mini Bayesian nonparametrics in Paris

Today, I attended a “miniworkshop” on Bayesian nonparametrics in Paris (Université René Descartes, now located in an intensely renovated area near the Grands Moulins de Paris), in connection with one of the ANR research grants that support my research, BANHDITS in the present case. Reflecting incidentally that it was the third Monday in a row that I was at a meeting listening to talks (after Hong Kong and Newcastle)… The talks were as follows

9h30 – 10h15 : Dominique Bontemps/Sébastien Gadat
Bayesian point of view on the Shape Invariant Model
10h15 – 11h : Pierpaolo De Blasi
Posterior consistency of nonparametric location-scale mixtures for multivariate density estimation
11h30 – 12h15 : Jean-Bernard Salomond
General posterior contraction rate Theorem in inverse problems.
12h15 – 13h : Eduard Belitser
On lower bounds for posterior consistency (I)
14h30 – 15h15 : Eduard Belitser
On lower bounds for posterior consistency (II)
15h15 – 16h : Judith Rousseau
Posterior concentration rates for empirical Bayes approaches
16h – 16h45 : Elisabeth Gassiat
Nonparametric HMM models

While most talks were focussing on contraction and consistency rates, hence far from my current interests, both talk by Judith and Elisabeth held more appeal to me. Judith gave conditions for an empirical Bayes nonparametric modelling to be consistent, with examples taken from Peter Green’s mixtures of Dirichlet, and Elisabeth concluded with a very generic result on the consistent estimation of a finite hidden Markov model. (Incidentally, the same BANHDITS grant will also support the satellite meeting on Bayesian non-parametric at MCMSki IV on Jan. 09.)