NDLI: Probabilistic interval-valued computation: toward a practical surrogate for statistics inside CAD tools

Content Provider	ACM Digital Library
Author	Rutenbar, Rob A. Fang, Claire F. Ma, James D. Singhee, Amith
Abstract	Interval methods offer a general, fine-grain strategy for modeling correlated range uncertainties in numerical algorithms. We present a new, improved interval algebra that extends the classical affine form to a more rigorous statistical foundation. Range uncertainties now take the form of confidence intervals. In place of pessimistic interval bounds, we minimize the probability of numerical "escape"; this can tighten interval bounds by 10X, while yielding 10-100X speedups over Monte Carlo. The formulation relies on three critical ideas: liberating the affine model from the assumption of symmetric intervals; a unifying optimization formulation; and a concrete probabilistic model. We refer to these as probabilistic intervals, for brevity. Our goal is to understand where we might use these as a surrogate for expensive, explicit statistical computations. Results from sparse matrices and graph delay algorithms demonstrate the utility of the approach, and the remaining challenges.
Starting Page	167
Ending Page	172
Page Count	6
File Format	PDF
ISBN	1595933816
DOI	10.1145/1146909.1146955
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2006-07-24
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Intervals Algorithms Dfm
Content Type	Text
Resource Type	Article

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Probabilistic interval-valued computation: toward a practical surrogate for statistics inside CAD tools