Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
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We introduced a generalised Wishart process (GWP) for modelling input dependent covariance matrices Σ(x), allowing one to model input varying correlations and uncertainties between multiple response variables. The GWP can naturally scale to thousands of response variables, as opposed to competing multivariate volatility models which are typically intractable for greater than 5 response variables. The GWP can also naturally capture a rich class of covariance dynamics --- periodicity, Brownian motion, smoothness, …--- through a covariance kernel.