Adaptive on-line learning of probability distributions from field theories

An adaptive algorithm is considered in on-line learning of probability functions, which infers a distribution underlying observed data x/sub 1/, x/sub 2/, ..., x/sub N/. The algorithm is based on how we can detect the change of a source function in an unsupervised learning scheme. This is an extension of an optimal on-line learning algorithm of probability distributions, which is derived from the field theoretical point of view. Since we learn not parameters of a model but probability functions themselves, the algorithm has the advantage that it requires no a priori knowledge of a model.