Comparison of Protein Structures by Mean Field Approximation

Estimation of Software Reliability by Sequential Testing with Simulated Annealing of Mean Field Approximation

Various problems of combinatorial optimization and permutation can be solved with neural network optimization. The problem of estimating the software reliability can be solved with the optimization of failed components to its minimum value. Various solutions of the problem of estimating the software reliability have been given. These solutions are exact and heuristic, but all the exact approach...

Mean Field Approximation for Fields of Experts

Information Geometry of Mean-Field Approximation

I present a general theory of mean-field approximation based on information geometry and applicable not only to Boltzmann machines but also to wider classes of statistical models. Using perturbation expansion of the Kullback divergence (or Plefka expansion in statistical physics), a formulation of mean-field approximation of general orders is derived. It includes in a natural way the "naive" me...

A Theory of Mean Field Approximation

I present a theory of mean field approximation based on information geometry. This theory includes in a consistent way the naive mean field approximation, as well as the TAP approach and the linear response theorem in statistical physics, giving clear information-theoretic interpretations to them.

A Mean Field Approximation of BCPNN

In this report we study a mean field (MF) approximation of the Bayesian Confidence Propagating Neural Network (BCPNN) for which we derive the energy function. This MF approximation is compared with the original formulation of the network in a number of different tasks in order to establish the similarities and dissimilarities. We investigate the effect of different updating strategies on the st...