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 ... more >>>

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 >>>