Storage, Representation, and Manipulation of Distribution Functions

Abstract

Probabilistic reasoning techniques are emerging as one of the most powerful ways to extract information from complex spatio-temporal data streams generated by modern sensors. Representing features and estimating their probability requires manipulating high-dimensional functions that have a priori unknown structure. This study examines alternate approaches to representing such distribution functions using implicit rather than explicit representations.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 2018
Accession Number
AD1057569

Entities

People

  • Rajit Manohar

Organizations

  • Yale University

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Accuracy
  • Air Force
  • Air Force Research Laboratories
  • Algorithms
  • Differential Equations
  • Distribution Functions
  • Energy Consumption
  • Government Procurement
  • Governments
  • Mathematics
  • Polynomials
  • Probability
  • Probability Distributions
  • Random Number Generators
  • Random Variables
  • Reasoning
  • Standards

Fields of Study

  • Computer science

Readers

  • Artificial Intelligence
  • Computational Modeling and Simulation
  • Quantum Dot Semiconductor Device Photonics and Graphene Optoelectronic Materials and THz Physics.