Welcome to the new version of CaltechAUTHORS. Login is currently restricted to library staff. If you notice any issues, please email coda@library.caltech.edu
Published 2004 | public
Book Section - Chapter Open

Communication and distributional complexity of joint probability mass functions

Abstract

The problem of truly-lossless (Pe = 0) distributed source coding [1] requires knowledge of the joint statistics of the sources. In particular the locations of the zeroes of the probability mass functions (pmfs) are crucial for encoding at rates below (H(X),H(Y)) [2]. We consider the distributed computation of the empirical joint pmf Pn of a sequence of random variable pairs observed at physically separated nodes of a network. We consider both worst-case and average measures of information exchange and treat both exact calculation of Pn and a notion of approximation. We find that in all cases the communication cost grows linearly with the size of the input. Further, we consider the problem of determining whether the empirical pmf has a zero in a particular location and show that in most cases considered this also requires a communication cost that is linear in the input size.

Additional Information

© Copyright 2004 IEEE. Reprinted with permission. Posted online: 2005-01-10. This work was supported by NSF Grant CCR-0220039 and a grant from the Lee Center for Advanced Networking at Caltech.

Files

JAGisit04.pdf
Files (254.4 kB)
Name Size Download all
md5:45f73bf154694efbf787b5e5c249f40f
254.4 kB Preview Download

Additional details

Created:
August 22, 2023
Modified:
October 16, 2023