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Many facets of Bayesian Modelling are firmly established in Machine Learning and give rise to state-of-the-art solutions to application problems. The sheer number of techniques, ideas and models which have been proposed, and the terminology, can be bewilde ...
Nowadays, state-of-the-art automatic speaker recognition systems show very good performance in discriminating between voices of speakers under controlled recording conditions. However, the conditions in which recordings are made in investigative activities ...
In this paper the perturbation influence properties of the stochastic model (variance-covariance) in linear models is discussed in detail. Some very useful formulae are established about the variance-covariance perturbation influence on the model parameter ...
In this paper, we propose a novel approach for solving the reliable broadcast problem in a probabilistic model, i.e., where links lose messages and where processes crash and recover probabilistically. Our approach consists in first defining the optimality ...
In this paper, we propose a novel approach for solving the reliable broadcast problem in a probabilistic unreliable model. Our approach consists in first defining the optimality of probabilistic reliable broadcast algorithms and the adaptiveness of algorit ...
Sparse approximations to Bayesian inference for nonparametric Gaussian Process models scale linearly in the number of training points, allowing for the application of these powerful kernel-based models to large datasets. We show how to generalize the binar ...
Department of Statistics, University of Berkeley, CA2004
Gaussian processes (GPs) are natural generalisations of multivariate Gaussian random variables to infinite (countably or continuous) index sets. GPs have been applied in a large number of fields to a diverse range of ends, and very many deep theoretical an ...
A vital task in sports video annotation is to detect and segment areas of the playfield. This is an important first step in player or ball tracking and detecting the location of the play on the playfield. In this paper we present a technique using statisti ...
We propose a semiparametric model for regression problems involving multiple response variables. Conditional dependencies between the responses are represented through a linear mixture of Gaussian processes. We propose an efficient approximate inference sc ...
We propose a semiparametric model for regression problems involving multiple response variables. The model makes use of a set of Gaussian processes that are linearly mixed to capture dependencies that may exist among the response variables. We propose an e ...
