Belief-propagation-guided Monte-Carlo sampling

A Monte Carlo algorithm for probabilistic propagation in belief networks based on importance sampling and stratified simulation techniques

A class of Monte Carlo algorithms for probability propagation in belief networks is given. The simulation is based on a two steps procedure. The rst one is a node deletion technique to calculate the 'a posteriori' distribution on a variable, with the particularity that when exact computations are too costly, they are carried out in an approximate way. In the second step, the computations done i...

Christiane Lemieux Monte Carlo and Quasi - Monte Carlo Sampling

Privacy-Preserving Belief Propagation and Sampling

We provide provably privacy-preserving versions of belief propagation, Gibbs sampling, and other local algorithms — distributed multiparty protocols in which each party or vertex learns only its final local value, and absolutely nothing else.

Importance Sampling Monte - Carlo Algorithms for the Calculation ofDempster - Shafer Belief

This paper presents importance sampling Monte-Carlo algorithms for the calculation of belief functions combination. When the connict between the evidence is not very high a simple Monte-Carlo algorithm can produce good quality estimations. For the case of highly connicting evidences a Markov chain Monte-Carlo algorithm was also proposed. In this paper, a new class of importance sampling based a...

Consensus Monte Carlo using expectation propagation∗

The article under discussion considers algorithms for performing inference on large or unwieldy datasets by partitioning the data, analyzing each piece separately, and then putting the inferences together. This is an active area of research in computational statistics (see, for example, Tresp, 2000, Ahn, Korattikara, and Welling, 2012, Gershman, Hoffman, and Blei, 2012, Korattikara, Chen, and W...