PUBLICATIONS

CTPPL: A Continuous Time Probabilistic Programming Language

Pfeffer, A.

Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence (IJCAI-09) (2009)

Probabilistic programming languages allow a modeler to build probabilistic models using complex data structures with all the power of a programming language. We present CTPPL, an expressive probabilistic programming language for dynamic processes that models processes using continuous time. Time is a first class element in our language; the amount of time taken by a subprocess can be specified using the full power of the language. We show through examples that CTPPL can easily represent existing continuous time frameworks and makes it easy to represent new ones. We present semantics for CTPPL in terms of a probability measure over trajectories. We present a particle filtering algorithm for the language that works for a large and useful class of CTPPL programs.

For More Information

To learn more or request a copy of a paper (if available), contact A. Pfeffer.

(Please include your name, address, organization, and the paper reference. Requests without this information will not be honored.)