Many probabilistic models introduce strong dependencies between variables using a latent multivariate Gaussian distribution or a Gaussian process. We present a new Markov chain Mo...
Iain Murray, Ryan Prescott Adams, David J. C. MacK...
This paper applies time-varying autoregressive (TVAR) models with stochastically evolving parameters to the problem of speech modeling and enhancement. The stochastic evolution mod...
Bayesian MLP neural networks are a flexible tool in complex nonlinear problems. The approach is complicated by need to evaluate integrals over high-dimensional probability distri...
Abstract. A nonparametric Bayesian extension of Independent Components Analysis (ICA) is proposed where observed data Y is modelled as a linear superposition, G, of a potentially i...
Our goal is to detect and track moving vehicles on a road observed from cameras placed on poles or buildings. Inter-vehicle occlusion is significant under these conditions and tra...