skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Numerical Investigation of Probability Measures Utilized in a Maximum Entropy Approach.

Abstract

Abstract not provided.

Authors:
; ;
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1399187
Report Number(s):
SAND2016-9914C
647989
DOE Contract Number:
AC04-94AL85000
Resource Type:
Conference
Resource Relation:
Conference: Proposed for presentation at the International Conference on Noise and Vibration Engineering held September 19-21, 2016 in Leuven, Belgium.
Country of Publication:
United States
Language:
English

Citation Formats

Bonney, Matthew, Brake, Matthew Robert, and Kammer, Danniel. Numerical Investigation of Probability Measures Utilized in a Maximum Entropy Approach.. United States: N. p., 2016. Web.
Bonney, Matthew, Brake, Matthew Robert, & Kammer, Danniel. Numerical Investigation of Probability Measures Utilized in a Maximum Entropy Approach.. United States.
Bonney, Matthew, Brake, Matthew Robert, and Kammer, Danniel. 2016. "Numerical Investigation of Probability Measures Utilized in a Maximum Entropy Approach.". United States. doi:. https://www.osti.gov/servlets/purl/1399187.
@article{osti_1399187,
title = {Numerical Investigation of Probability Measures Utilized in a Maximum Entropy Approach.},
author = {Bonney, Matthew and Brake, Matthew Robert and Kammer, Danniel},
abstractNote = {Abstract not provided.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = 2016,
month =
}

Conference:
Other availability
Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.

Save / Share:
  • Abstract not provided.
  • In this paper, we described the PNNL Word Sense Disambiguation system as applied to the English All-Word task in Se-mEval 2007. We use a supervised learning approach, employing a large number of features and using Information Gain for dimension reduction. Our Maximum Entropy approach combined with a rich set of features produced results that are significantly better than baseline and are the highest F-score for the fined-grained English All-Words subtask.
  • In future computing environments where computer resources are abundant, a linear programming (LP) approach to maximum entropy signal/image restoration could have advantages over traditional techniques. A revised simplex LP algorithm with inequality constraints is presented. Dantzig's bounded-variable method is used to express the maximum entropy restoration problem as a LP problem. This is done by approximating the nonlinear objective function with piecewise linear segments, then bounding the variables as a function of the number of segments used. Linear inequality constraints may be used to assure a basic feasible solution. Experimental results with 512-point signals are presented. These include restorations ofmore » noisy signals. Problems with as many as 513 equations and 6144 unknowns are demonstrated. The complexity of the LP restoration approach is briefly addressed. 14 refs., 4 figs.« less
  • We study the continuity of an abstract generalization of the maximum-entropy inference—a maximizer. It is defined as a right-inverse of a linear map restricted to a convex body which uniquely maximizes on each fiber of the linear map a continuous function on the convex body. Using convex geometry we prove, amongst others, the existence of discontinuities of the maximizer at limits of extremal points not being extremal points themselves and apply the result to quantum correlations. Further, we use numerical range methods in the case of quantum inference which refers to two observables. One result is a complete characterization ofmore » points of discontinuity for 3 × 3 matrices.« less
  • Abstract not provided.