This paper gives a probabilistic interpretation of a class of nite diierence schemes often referred to as the-method. In particular, the present paper shows that for some parameter values the-method can been seen as a binomial tree with a random time.

K-means is one of the most widely used algorithms for clustering in Data Mining applications, which attempts to minimize the sum of square of Euclidean distance of the points in the clusters from the respective means of the clusters. The simplicity and scalability of K-means makes it very appealing. However, K-means suffers from local minima problem, and comes with no guarantee to converge to t...

An approximation method is presented for probabilistic inference with continuous random variables. These problems can ari.se in many practical problems, in particular where there are "second order" probabilities. The approximation, based on the Gaussian influence diagram, iterates over linear approximations to the inference problem.

We have recently developed several ways of performing Canonical Correlation Analysis [1, 5, 7, 4] with probabilistic methods rather than the standard statistical tools. However, the computational demands of training such methods scales with the square of the number of samples, making these methods uncompetitive with e.g. artificial neural network methods [3, 2]. In this paper, we examine a rece...