Estimating Random Variables from Random Sparse Observations

Details Estimating Random Variables from Random Sparse Observations Estimating Random Variables from Random Sparse Observations

2007-09-03

arxiv.org/abs/0709.0145v1

Computer Science

Information Theory

22 pages, 1 eps figures, invited paper for European Transactions on Telecommunications

Scientific paper

Let X_1,...., X_n be a collection of iid discrete random variables, and Y_1,..., Y_m a set of noisy observations of such variables. Assume each observation Y_a to be a random function of some a random subset of the X_i's, and consider the conditional distribution of X_i given the observations, namely \mu_i(x_i)\equiv\prob\{X_i=x_i|Y\} (a posteriori probability). We establish a general relation between the distribution of \mu_i, and the fixed points of the associated density evolution operator. Such relation holds asymptotically in the large system limit, provided the average number of variables an observation depends on is bounded. We discuss the relevance of our result to a number of applications, ranging from sparse graph codes, to multi-user detection, to group testing.

Affiliated with

Also associated with

No associations

Rating

Estimating Random Variables from Random Sparse Observations does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Estimating Random Variables from Random Sparse Observations, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Estimating Random Variables from Random Sparse Observations will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-467854