Online Student-$t$ Processes with an Overall-local Scale Structure for Modelling Non-stationary Data
TL;DR: We introduce a mixture of TPs with an SMC sampler so that we may take advantage of the additional flexibility of a mixture-of-experts model with a convenient online inference algorithm.
Abstract: Mixture-of-expert (MOE) models are popular methods in machine learning, since they can model heterogeneous behaviour across the space of the data using an ensemble collection of learners. These models are especially useful for modelling dynamic data as time-dependent data often exhibit non-stationarity and heavy-tailed errors, which may be inappropriate to model with a typical single expert model. We propose a mixture of Student-$t$ processes with an adaptive structure for the covariance and noise behaviour for each mixture. Moreover, we use a sequential Monte Carlo (SMC) sampler to perform online inference as data arrive in real time. We demonstrate the superiority of our proposed approach over other models on synthetic and real-world datasets to prove the necessity of the novel method.
Submission Number: 380
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