Boltzmann machines (BMs) are a class of binary neural networks for which
there have been numerous proposed methods of estimation. Recently, it has been
shown that in the fully visible case of the BM, the method of maximum
pseudolikelihood estimation (MPLE) results in parameter estimates which are
consistent in the probabilistic sense. In this article, we investigate the
properties of MPLE for the fully visible BMs further, and prove that MPLE also
yields an asymptotically normal parameter estimator. These results can be used
to construct confidence intervals and to test statistical hypotheses. We
support our theoretical results by showing that the estimator behaves as
expected in a simulation study