Book Review: Gaussian random processes

Random Feature Expansions for Deep Gaussian Processes

The composition of multiple Gaussian Processes as a Deep Gaussian Process (DGP) enables a deep probabilistic nonparametric approach to flexibly tackle complex machine learning problems with sound quantification of uncertainty. Existing inference approaches for DGP models have limited scalability and are notoriously cumbersome to construct. In this work we introduce a novel formulation of DGPs b...

Matching wavelet packets to Gaussian random processes

In this paper, we consider the problem of approximating a set of arbitrary, discrete-time, Gaussian random processes by a single, representative wavelet-based, Gaussian process. We measure the similarity between the original processes and the wavelet-based process with the Bhattacharyya coefficient. By manipulating the Bhattacharyya coefficient, we reduce the task of defining the representative...

Techniques for treating non-Gaussian random processes

Autoregressive Gaussian Random Vectors of First Order

Book Review

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