Oded Goldreich, Dana Ron

Following Feige, we consider the problem of

estimating the average degree of a graph.

Using ``neighbor queries'' as well as ``degree queries'',

we show that the average degree can be approximated

arbitrarily well in sublinear time, unless the graph is extremely sparse

(e.g., unless the graph has a sublinear ...
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Arnab Bhattacharyya, Sutanu Gayen, Kuldeep S. Meel, Dimitrios Myrisiotis, A. Pavan, N. V. Vinodchandran

In this paper, we establish a novel connection between total variation (TV) distance estimation and probabilistic inference. In particular, we present an efficient, structure-preserving reduction from relative approximation of TV distance to probabilistic inference over directed graphical models. This reduction leads to a fully polynomial randomized approximation scheme (FPRAS) for ... more >>>