#### AI Seminar

# Toyota AI Seminar: Efficient Implementation of and Inference in Probabilistic Programming Languages

Add to Google Calendar

Probabilistic programming languages simplify the development

of probabilistic models by allowing programmers to specify a

stochastic process using syntax that resembles modern

programming languages. These languages allow programmers to

freely mix deterministic and stochastic elements, resulting

in tremendous modeling flexibility. The resulting programs

define prior distributions: running the (unconditional)

program forward many times results in a distribution over

execution traces, with each trace generating a sample of

data from the prior. The goal of inference in such programs

is to reason about the posterior distribution over execution

traces conditioned on a particular program output —

essentially "running the program backwards."

In this talk, I will discuss a general technique for turning

any programming language into a probabilistic programming

language with an accompanying universal Markov chain Monte

Carlo inference engine. The method allows the full use of

all language constructs permitted by the original

(non-probabilistic) language. I will illustrate the

technique by discussing Stochastic Matlab, a new imperative

probabilistic programming language, and will show examples

of probabilistic programming applied to problems in

planning, vision and machine learning.

This is joint work with Noah Goodman, Andreas Stuhlmueller,

and Joshua Tenenbaum.

David Wingate received a B.S. and M.S. in Computer Science from

Brigham Young University in 2002 and 2004, and a Ph.D. in

Computer Science from University of Michigan in 2008. He is

currently a research scientist at MIT with a joint appointment in

the Computational Cognitive Science group and Laboratory for

Information Decision Systems.

His research interests lie at the intersection of perception, control

and learning. He has mostly recently focused on probabilistic

programming and its application to reinforcement learning, dynamical

systems modeling and machine learning.