A PLUG-AND-PLAY ARCHITECTURE FOR PROBABILISTIC PROGRAMMING

Abstract

In the probabilistic-programming paradigm, the application logic is specified by means of a description of a probabilistic model (by stating how a sample is being produced) using a Probabilistic Programming Language (PPL). The principal value one obtains from a probabilistic program lies in the inference thereof, that is, reasoning about the entire probability distribution that the program defines (e.g., finding a likely event or estimating its marginal probability). The PPAML kickoff meeting highlighted several research challenges regarding the development of inference infrastructure for PPL, for both increasing software efficiency and reducing software complexity, towards the goal of broadening the PPL applications and the community of implementers and programmers. These challenges include the design of an Application Program Interface (API), or alternatively an Intermediate Representation Language (IRL), that would allow new solvers to be plugged into existing PPLs, and for PPL engines to be able to pick from and combine solvers for a given problem.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 2017
Accession Number
AD1031379

Entities

People

  • Benny Kimelfeld
  • Emir Pasalic
  • Molham Aref
  • Yannis Kassios
  • Zografoula Vagena

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Application Software
  • Artificial Intelligence
  • Computer Programming
  • Computer Programs
  • Data Sets
  • Databases
  • Language
  • Machine Learning
  • Models
  • Monte Carlo Method
  • Probabilistic Models
  • Probability
  • Probability Distributions
  • Programming Languages
  • Random Variables

Fields of Study

  • Computer science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Artificial Intelligence
  • Software Engineering.

Technology Areas

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