We introduce a scalable approach to Gaussian process inference that combines spatio-
temporal filtering with natural gradient variational inference, resulting in a non-conjugate GP …

Approximate Bayesian inference methods that scale to very large datasets are crucial in
leveraging probabilistic models for real-world time series. Sparse Markovian Gaussian …

We formulate approximate Bayesian inference in non-conjugate temporal and spatio-
temporal Gaussian process models as a simple parameter update rule applied during …

This letter puts forward a new method to estimate the direction of arrivals (DOAs) along with
the source number under wideband circumstances. The method constructs a multiple-input …

Effective noise reduction and speech enhancement algorithms have great potential to
enhance lives of hearing aid users by restoring speech intelligibility. An open problem in …

This thesis concerns the use of Gaussian processes (GPs) as distributions over unknown
functions in Machine Learning and probabilistic modeling. GPs have been found to have …

Spatio-temporal phenomena are ubiquitous in the world around us. To successfully model
them, it is necessary to exploit as much information as possible, whether in the form of …

The extended Kalman filter (EKF) is a classical signal processing algorithm which performs
efficient approximate Bayesian inference in non-conjugate models by linearising the local …

This thesis presents a probability model for generating polyphonic audio and derives an
algorithm for estimating the most probable interpretation of new audio under this model. The …

Models and inference for temporal Gaussian processes - (ie, GPs for signal processing) Page 1
Models and inference for temporal Gaussian processes (ie, GPs for signal processing) William …