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Showing 1 to 3 of 3 for “"Gaussian Process State Space Model"”.

  1. Bayesian Time Series Learning with Gaussian Processes

    … designed to learn Bayesian nonparametric models of time series. The goal of these kinds of models is twofold. First, they aim at making predictions which quantify the uncertainty due to limitations in the quantity and the quality of the data. Second, they are flexible enough to model

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  2. Sparse Gaussian Process Approximations and Applications

    … use probability distributions to represent the state of uncertainty that a learning agent is in. In particular, we will investigate methods which use Gaussian processes to represent distributions over functions. Gaussian process models require approximations in order to be practically useful. …

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  3. Variational Inference in Dynamical Systems

    … applied to dynamical systems in general, and state space models whose transition function is drawn from a Gaussian process (GPSSM) in particular. We show bias can derive from assuming posteriors in non-linear systems to be jointly Gaussian, and from assuming that we can sever the dependence …

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