A new, general approach is described for approximate inference in first-order probabilistic languages, using Markov chain Monte Carlo (MCMC) techniques in the space of concrete po...
Abstract. We use a Markov Chain Monte Carlo (MCMC) MML algorithm to learn hybrid Bayesian networks from observational data. Hybrid networks represent local structure, using conditi...
For a large class of examples arising in statistical physics known as attractive spin systems (e.g., the Ising model), one seeks to sample from a probability distribution π on an...
In this paper we present Poisson sum series representations for α-stable (αS) random variables and α-stable processes, in particular concentrating on continuous-time autoregres...
We present a methodology for a sensor network to answer queries with limited and stochastic information using probabilistic techniques. This capability is useful in that it allows...