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Gaussian Processes (GP) are a nonparametric supervised learning method used to solve regression and probabilistic classification problems. The advantages of Gaussian processes are: The prediction interpolates the observations (at least for regular kernels).
The GP Model ¶. The components of a user built (Exact, i.e. non-variational) GP model in GPyTorch are, broadly speaking: An __init__ method that takes the training data and a likelihood, and constructs whatever objects are necessary for the model’s forward method.
In GPyTorch, defining a GP involves extending one of our abstract GP models and defining a forward method that returns the prior. For deep GPs, things are similar, but there are two abstract GP models that must be overwritten: one for hidden layers and one for the deep GP model itself.
26 de jan. de 2021 · G aussian Process (GP) is a powerful supervised machine learning method that is largely used in regression settings. This method is desirable in practice since: it performs quite well in small data regime; it is highly interpretable; it automatically estimates the prediction uncertainty.
Defining the SGPR Model¶ We now define the GP model. For more details on the use of GP models, see our simpler examples. This model constructs a base scaled RBF kernel, and then simply wraps it in an InducingPointKernel. Other than this, everything should look the same as in the simple GP models.
5 de jan. de 2021 · Gaussian processes (GPs) are a flexible class of nonparametric machine learning models commonly used for modeling spatial and time series data. A common application of GPs is regression.
