We present an analysis of concentration-of-expectation phenomena in layered Bayesian networks that use generalized linear models as the local conditional ...

We present an analysis of concentration-of-expectation phenomena in layered Bayesian networks that use generalized linear models as the local.

Bibliographic details on On the Concentration of Expectation and Approximate Inference in Layered Networks.

PDF | We present a class of approximate inference algorithms for graphical models of the QMR-DT type. We give convergence rates for these algorithms and.

Algorithms for approximate probabilistic inference that exploit averaging phenomena occurring at nodes with large numbers of parents are given, ...

Approximate inference algorithms for two-layer bayesian networks. In NIPS ... On the concentration of expectation and approximate inference in layered networks.

On the concentration of expectation and approximate inference in layered Bayesian networks. ... Approximate inference algorithms for two-layer Bayesian networks.

Oct 11, 2012 · Abstract. A random variable is sampled from a discrete distribution. The missing mass is the probability of the set of points not observed ...

Jan 9, 2013 · A random variable is sampled from a discrete distribution. The missing mass is the probability of the set of points not observed in the ...

On the Concentration of Expectation and Approximate Inference in Layered Networks · X. NguyenMichael I. Jordan. Computer Science, Mathematics. NIPS. 2003. TLDR.