Gaussian processes for machine learning

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Gaussian processes for machine learning (2003)

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by Carl Edward Rasmussen

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639 - 2 self

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@MISC{Rasmussen03gaussianprocesses,
    author = {Carl Edward Rasmussen},
    title = {Gaussian processes for machine learning},
    year = {2003}
}

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Abstract

We give a basic introduction to Gaussian Process regression models. We focus on understanding the role of the stochastic process and how it is used to define a distribution over functions. We present the simple equations for incorporating training data and examine how to learn the hyperparameters using the marginal likelihood. We explain the practical advantages of Gaussian Process and end with conclusions and a look at the current trends in GP work.

Keyphrases

gaussian process machine learning discrete output simple equation current trend neural network continuous output complex data set marginal likelihood basic introduction practical advantage complex counterpart expressive power gaussian process regression model training data stochastic process possible advantage simple parametric model complex problem data set important constituent gp work

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