Estimating Random Variables from Random Sparse Observations

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.

💡 Research Summary

The paper investigates a very general statistical model in which a collection of independent and identically distributed (i.i.d.) discrete random variables (X_1,\dots ,X_n) is observed through a set of noisy measurements (Y_1,\dots ,Y_m). Each measurement (Y_a) is a random function of a randomly chosen subset (\partial a\subset{1,\dots ,n}) of the variables together with an independent noise term (Z_a):
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