Applied Nonparametric Bayes
Computer Science has historically been strong on data structures and weak on inference from data, whereas Statistics has historically
been weak on data structures and strong on inference from data. One way to draw on the strengths of both disciplines is to develop
``inferential methods for data structures''; i.e., methods that are based on probability distributions on recursively-defined
objects such as trees, graphs, grammars and function calls. This is accommodated in the world of ``nonparametric Bayes,''
where prior and posterior distributions are allowed to be general stochastic processes. In this talk I discuss a variety of applied
problems that are naturally tackled from this point of view.
I will discuss nonparametric Bayesian solutions to problems in natural language parsing, computational vision, information
retrieval, statistical genetics and protein structural modeling.
Michael I. Jordan, University of California, Berkeley
