Learning subjective probabilities from a small data set

Abstract

A method to learn subjective probabilities from sparse data is discussed. The method is based on the principle of Information Diffusion and exhibits a complexity O(n logn). A way of generating families of Information Diffusion functions is discussed. Applications on real data and varying number of samples are shown.