This paper argues in favor of the thesis that two different concepts of conditional interval probability are needed, in order to serve the huge variety of tasks conditional probab...
In this paper, variable bit rate (VBR) H.261 encoded video traffic is modeled by a nonlinear time series process. A threshold autoregressive (TAR) process is of particular interes...
Jimmie L. Davis, Kavitha Chandra, Charles Thompson
Published results show that various models may be obtained by combining parallel composition with probability and with or without non-determinism. In this paper we treat this probl...
Abstract. We use a Markov Chain Monte Carlo (MCMC) MML algorithm to learn hybrid Bayesian networks from observational data. Hybrid networks represent local structure, using conditi...
We present a simple new Monte Carlo algorithm for evaluating probabilities of observations in complex latent variable models, such as Deep Belief Networks. While the method is bas...