CTPPL: A Continuous Time Probabilistic Programming Language

Abstract

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.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 2009
Accession Number
ADA531328

Entities

People

  • Avi Pfeffer

Organizations

  • Harvard University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Bayesian Networks
  • Computer Programming
  • Computer Science
  • Filtration
  • Language
  • Mathematics
  • Models
  • Monte Carlo Method
  • Particles
  • Probabilistic Models
  • Probability
  • Probability Distributions
  • Programming Languages
  • Semantics
  • Trajectories

Fields of Study

  • Computer science
  • Engineering

Readers

  • Computational Linguistics
  • Materials Science.
  • Mathematical Modeling and Probability Theory.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Machine Translation