This paper proposes a Bayesian algorithm to estimate the parameters of a smooth transition regression model. With in this model, time series are divided into segments and a linear...
We consider the problem of learning multiscale graphical models. Given a collection of variables along with covariance specifications for these variables, we introduce hidden var...
Myung Jin Choi, Venkat Chandrasekaran, Alan S. Wil...
In this paper, an efficient method for language model lookahead probability generation is presented. Traditional methods generate language model look-ahead (LMLA) probabilities fo...
This paper presents a new framework for shape modeling and analysis. A shape instance is described by a curvature-based shape descriptor. A Profile Hidden Markov Model (PHMM) is ...
This paper introduces a discriminative extension to whole-word point process modeling techniques. Meant to circumvent the strong independence assumptions of their generative prede...