Bayes in Grenoble: Robin Ryder

on the October 24, 2019

October, 24
Robin Ryder from Paris-Dauphine University will talk about "A Bayesian non-parametric methodology for inferring grammar complexity". The event will take place on October, 24 at 2:00 pm at IMAG building.
Bayes in Grenoble is a new reading group on Bayesian statistical methods. 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. Feel free to contact them if you wish to attend/be added to the mailing list and/or give a talk.

React on social media: #BIGseminar

24 October 2019, Robin Ryder (Université Paris-Dauphine)

A Bayesian non-parametric methodology for inferring grammar complexity

Based on a set of strings from a language, we wish to infer the complexity of the underlying grammar. To this end, we develop a methodology to choose between two classes of formal grammars in the Chomsky hierarchy: simple regular grammars and more complex context-free grammars. To do so, we introduce a probabilistic context-free grammar model in the form of a Hierarchical Dirichlet Process over rules expressed in Greibach Normal Form. In comparison to other representations, this has the advantage of nesting the regular class within the context-free class. We consider model comparison both by exploiting this nesting, and with Bayes' factors. The model is fit using a Sequential Monte Carlo method, implemented in the Birch probabilistic programming language. We apply this methodology to data collected from primates, for which the complexity of the grammar is a key question.

Published on January 9, 2020

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Room 106
IMAG building