In applications of graphical models arising in fields such as computer vision, the hidden variables of interest are most naturally specified by continuous, non--Gaussian distributions. However, due to the limitations of existing inf#6F6F3 algorithms, it is of#]k necessary tof#3# coarse, discrete approximations to such models. In this paper, we develop a nonparametric belief propagation (NBP) algorithm, which uses stochastic methods to propagate kernel--based approximations to the true continuous messages. Each NBP message update is based on an efficient sampling procedure which can accomodate an extremely broad class of potentialf#l3]k[[z3 allowing easy adaptation to new application areas. We validate our method using comparisons to continuous BP for Gaussian networks, and an application to the stereo vision problem.

The clique tree algorithm is the standard method for doing inference in Bayesian networks. It works by manipulating clique potentials — distributions over the variables in a clique. While this approach works well for many networks, it is limited by the need to maintain an exact representation of the clique potentials. This paper presents a new unified approach that combines approximate inference and the clique tree algorithm, thereby circumventing this limitation. Many known approximate inference algorithms can be viewed as instances of this approach. The algorithm essentially does clique tree propagation, using approximate inference to estimate the densities in each clique. In many settings, the computation of the approximate clique potential can be done easily using statistical importance sampling. Iterations are used to gradually improve the quality of the estimation. 1

In this paper we study a new general class of algorithms for the propagation of probabilities on graphical structures based on importance sampling techniques. The idea is to make an approximate and fast propagation in order to obtain a sampling distribution as close as possible to the true one. Our proposal is based on a deletion sequence of the variables to calculate the 'a posteriori' probability in one variable. The deletion procedure is the basis for the exact propagation algorithms. Here the difference is that sometimes, when the cost of the exact deletion exceeds a given limit, an approximated deletion is done. The calculations of the deletion procedure will be used to obtain in a very fast way a sample for the simulation. Some experimental tests are carried out to compare our procedure with other known methods. 1 INTRODUCTION Probability propagation in belief networks consists on updating the probability values of the variables in a dependence graph, given some variables that h...

A class of Monte Carlo algorithms for probability propagation in belief networks is given. The simulation is based on a two steps procedure. The first 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 in the first one are used to obtain random configurations for the variables of interest. These configurations are weighted according to the importance sampling methodology. Different particular algorithms are obtained depending on the approximation procedure used in the first step and in the way of obtaining the random configurations. In this last case, a stratified sampling technique is used, which has been adapted to be applied to very large networks without problems with rounding errors.

The clique tree algorithm is the standard method for doing inference in Bayesian networks. It works by manipulating clique potentials --- distributions over the variables in a clique. While this approach works well for many networks, it is limited by the need to maintain an exact representation of the clique potentials. This paper presents a new unified approach that combines approximate inference and the clique tree algorithm, thereby circumventing this limitation. Many known approximate inference algorithms can be viewed as instances of this approach. The algorithm essentially does clique tree propagation, using approximate inference to estimate the densities in each clique. In many settings, the computation of the approximate clique potential can be done easily using statistical importance sampling. Iterations are used to gradually improve the quality of the estimation. 1 Introduction Bayesian networks (BNs) allow us to represent complex probabilistic models compa...

The paper introduces a family of approximate schemes that extend the process of computing sample mean in importance sampling from the conventional OR space to the AND/OR search space for graphical models. All the sample means are defined on the same set of samples and trade time with variance. At one end is the AND/OR sample tree mean which has the same time complexity as the conventional OR sample tree mean but has lower variance. At the other end is the AND/OR sample graph mean which requires more time to compute but has the lowest variance. The paper provides theoretical analysis as well as empirical evaluation demonstrating that the AND/OR sample tree and graph means are far closer to the true mean than the OR sample tree mean.