title: Integration of stochastic models by minimizing alpha-divergence.
abstract: When there are a number of stochastic models in the form of probability distributions, one needs to integrate them. Mixtures of distributions are frequently used, but exponential mixtures also provide a good means of integration. This letter proposes a one-parameter family of integration, called alpha-integration, which includes all of these well-known integrations. These are generalizations of various averages of numbers such as arithmetic, geometric, and harmonic averages. There are psychophysical experiments that suggest that alpha-integrations are used in the brain. The alpha-divergence between two distributions is defined, which is a natural generalization of Kullback-Leibler divergence and Hellinger distance, and it is proved that alpha-integration is optimal in the sense of minimizing alpha-divergence. The theory is applied to generalize the mixture of experts and the product of experts to the alpha-mixture of experts. The alpha-predictive distribution is also stated in the Bayesian framework.
authors: RIKEN Brain Science Institute, Wako-shi, Hirosawa 2-1, Saitama 351-0198, Japan. amari@brain.riken.jp
authors: Shun-ichi
authors: S
authors: Amari
mesh_terms: D008962:Models, Theoretical; D011336:Probability; D013269:Stochastic Processes