A General Algorithm for Approximate Inference and its Application to
  Hybrid Bayes Nets
  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.
