  Solving the error correcting code is an important goal with regard to
communication theory.To reveal the error correcting code characteristics,
several researchers have applied a statistical-mechanical approach to this
problem. In our research, we have treated the error correcting code as a Bayes
inference framework. Carrying out the inference in practice, we have applied
the NMF (naive mean field) approximation to the MPM (maximizer of the posterior
marginals) inference, which is a kind of Bayes inference. In the field of
artificial neural networks, this approximation is used to reduce computational
cost through the substitution of stochastic binary units with the deterministic
continuous value units. However, few reports have quantitatively described the
performance of this approximation. Therefore, we have analyzed the
approximation performance from a theoretical viewpoint, and have compared our
results with the computer simulation.
