Invention Grant
- Patent Title: Methods, systems, and computer readable media for non-parametric dependence detection using bitwise operations in a computing system
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Application No.: US15926162Application Date: 2018-03-20
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Publication No.: US10296555B1Publication Date: 2019-05-21
- Inventor: Kai Zhang , Michael Thomas Max Baiocchi , Zhigen Zhao
- Applicant: The University of North Carolina at Chapel Hill , THE BOARD OF TRUSTEES OF THE LELAND STANFORD JUNIOR UNIVERSITY , TEMPLE UNIVERSITY - OF THE COMMONWEALTH SYSTEM OF HIGHER EDUCATION
- Applicant Address: US NC Chapel Hill US CA Palo Alto US PA Philadelphia
- Assignee: The University of North Carolina at Chapel Hill,The Board of Trustees of the Leland Stanford Junior University,Temple University—Of the Commonwealth System of Higher Education
- Current Assignee: The University of North Carolina at Chapel Hill,The Board of Trustees of the Leland Stanford Junior University,Temple University—Of the Commonwealth System of Higher Education
- Current Assignee Address: US NC Chapel Hill US CA Palo Alto US PA Philadelphia
- Agency: Jenkins, Wilson, Taylor & Hunt P.A.
- Main IPC: G06K9/62
- IPC: G06K9/62 ; G06F17/15 ; G06F17/18

Abstract:
Methods, systems, and computer readable media for non-parametric dependence detection using bitwise operations in a computing system are disclosed. One method for non-parametric dependence detection using bitwise operations in a computing system includes receiving a set of p variables, wherein p represents an integer greater than or equal to two. The method also includes generating a set of binary interaction designs (BIDs) using a depth value d and bitwise operations, wherein each of the set of BIDs indicates a dependence structure based on arrangement of partitions in the respective BID. The method further includes determining, using the BIDs generated using bitwise operations in a computing system, non-parametric dependence between the set of p variables. The method also includes performing data analysis involving the set of p variables using the non-parametric dependence between the set of p variables. The method further includes generating output related to the data analysis.
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