Storage, Representation, and Manipulation of Distribution Functions
Technical Report,01 May 2017,28 Feb 2018
Yale University New Haven United States
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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.
- Statistics and Probability