Accession Number:

AD1057569

Title:

Storage, Representation, and Manipulation of Distribution Functions

Descriptive Note:

Technical Report,01 May 2017,28 Feb 2018

Corporate Author:

Yale University New Haven United States

Personal Author(s):

Report Date:

2018-08-01

Pagination or Media Count:

15.0

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.

Subject Categories:

  • Statistics and Probability

Distribution Statement:

APPROVED FOR PUBLIC RELEASE