Transforms of pseudo-Boolean random variables

Authors:
Guoli Ding;R. F. Lax;Jianhua Chen;Peter P. Chen;Brian D. Marx
Affiliations:
Department of Mathematics, LSU, Baton Rouge, LA 70803, United States;Department of Mathematics, LSU, Baton Rouge, LA 70803, United States;Department of Computer Science, 298 Coates Hall, LSU, Baton Rouge, LA 70803, United States;Department of Computer Science, 298 Coates Hall, LSU, Baton Rouge, LA 70803, United States;Department of Experimental Statistics, LSU, Baton Rouge, LA 70803, United States
Venue:
Discrete Applied Mathematics
Year:
2010

Citing 6
Cited 1

The mathematics of nonlinear programming

The mathematics of nonlinear programming
Equivalent Representations of Set Functions

Mathematics of Operations Research
Spectral Techniques in Digital Logic

Spectral Techniques in Digital Logic
Best approximations of fitness functions of binary strings

Natural Computing: an international journal
Formulas for approximating pseudo-Boolean random variables

Discrete Applied Mathematics
Approximating pseudo-Boolean functions on non-uniform domains

IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence

Supporting velocity of investigation with behavior analysis of malware

Proceedings of the Seventh Annual Workshop on Cyber Security and Information Intelligence Research

Quantified Score

Hi-index	0.04

Visualization

Abstract

As in earlier works, we consider {0,1}^n as a sample space with a probability measure on it, thus making pseudo-Boolean functions into random variables. Under the assumption that the coordinate random variables are independent, we show it is very easy to give an orthonormal basis for the space of pseudo-Boolean random variables of degree at most k. We use this orthonormal basis to find the transform of a given pseudo-Boolean random variable and to answer various least squares minimization questions.