Posts tagged gaussian processes

Gaussian Processes: Latent Variable Implementation

The gp.Latent class is a direct implementation of a Gaussian process without approximation. Given a mean and covariance function, we can place a prior on the function \(f(x)\),

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Marginal Likelihood Implementation

The gp.Marginal class implements the more common case of GP regression: the observed data are the sum of a GP and Gaussian noise. gp.Marginal has a marginal_likelihood method, a conditional method, and a predict method. Given a mean and covariance function, the function \(f(x)\) is modeled as,

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