title: Estimation and marginalization using the Kikuchi approximation methods.
abstract: In this letter, we examine a general method of approximation, known as the Kikuchi approximation method, for finding the marginals of a product distribution, as well as the corresponding partition function. The Kikuchi approximation method defines a certain constrained optimization problem, called the Kikuchi problem, and treats its stationary points as approximations to the desired marginals. We show how to associate a graph to any Kikuchi problem and describe a class of local message-passing algorithms along the edges of any such graph, which attempt to find the solutions to the problem. Implementation of these algorithms on graphs with fewer edges requires fewer operations in each iteration. We therefore characterize minimal graphs for a Kikuchi problem, which are those with the minimum number of edges. We show with empirical results that these simpler algorithms often offer significant savings in computational complexity, without suffering a loss in the convergence rate. We give conditions for the convexity of a given Kikuchi problem and the exactness of the approximations in terms of the loops of the minimal graph. More precisely, we show that if the minimal graph is cycle free, then the Kikuchi approximation method is exact, and the converse is also true generically. Together with the fact that in the cycle-free case, the iterative algorithms are equivalent to the well-known belief propagation algorithm, our results imply that, generically, the Kikuchi approximation method can be exact if and only if traditional junction tree methods could also solve the problem exactly.
authors.affiliation: Electrical Engineering and Computer Science, Department, University of California, Berkeley, CA 94720, USA. payamp@eecs.berkeley.edu
authors.forename: Payam
authors.initials: P
authors.lastname: Pakzad
authors.forename: Venkat
authors.initials: V
authors.lastname: Anantharam
mesh_terms: D000465:Algorithms; D055592:Biophysical Phenomena; D001703:Biophysics; D003198:Computer Simulation; D006801:Humans; D015233:Models, Statistical; D016571:Neural Networks, Computer; D011336:Probability