4 citations · 10 across the 6 of their papers we have counts for
9 papers
Spatio-Temporal Variational Gaussian Processes
Oliver Hamelijnck, William J. Wilkinson, Niki A. Loppi +2
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…
Bayes-Newton Methods for Approximate Bayesian Inference with PSD Guarantees
William J. Wilkinson, Simo Särkkä, Arno Solin
We formulate natural gradient variational inference (VI), expectation propagation (EP), and posterior linearisation (PL) as extensions of Newton's method for optimising the paramet…
Sparse Algorithms for Markovian Gaussian Processes
William J. Wilkinson, Arno Solin, Vincent Adam
Approximate Bayesian inference methods that scale to very large datasets are crucial in leveraging probabilistic models for real-world time series. Sparse Markovian Gaussian proces…
Fast Variational Learning in State-Space Gaussian Process Models
Paul E. Chang, William J. Wilkinson, Mohammad Emtiyaz Khan +1
Gaussian process (GP) regression with 1D inputs can often be performed in linear time via a stochastic differential equation formulation. However, for non-Gaussian likelihoods, thi…
State Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian Processes
William J. Wilkinson, Paul E. Chang, Michael Riis Andersen +1
We formulate approximate Bayesian inference in non-conjugate temporal and spatio-temporal Gaussian process models as a simple parameter update rule applied during Kalman smoothing.…
Movement Tracking by Optical Flow Assisted Inertial Navigation
Lassi Meronen, William J. Wilkinson, Arno Solin
Robust and accurate six degree-of-freedom tracking on portable devices remains a challenging problem, especially on small hand-held devices such as smartphones. For improved robust…