pymc.gp.TP#
- class pymc.gp.TP(*, mean_func=<pymc.gp.mean.Zero object>, scale_func=<pymc.gp.cov.Constant object>, cov_func=None, nu=None)[source]#
Student’s T process prior.
The usage is nearly identical to that of gp.Latent. The differences are that it must be initialized with a degrees of freedom parameter, and TP is not additive. Given a mean and covariance function, and a degrees of freedom parameter, the function \(f(x)\) is modeled as,
\[f(X) \sim \mathcal{TP}\left( \mu(X), k(X, X'), \nu \right)\]- Parameters:
- mean_func
Mean, defaultZero The mean function.
- scale_func2D array_like, or
Covariance, defaultConstant The covariance function.
- cov_func2D array_like, or
Covariance, defaultNone Deprecated, previous version of “scale_func”
- nu
float The degrees of freedom
- mean_func
References
Shah, A., Wilson, A. G., and Ghahramani, Z. (2014). Student-t Processes as Alternatives to Gaussian Processes. arXiv preprint arXiv:1402.4306.
Methods
TP.__init__(*[, mean_func, scale_func, ...])TP.conditional(name, Xnew[, jitter])Return the conditional distribution evaluated over new input locations Xnew.
TP.marginal_likelihood(name, X, *args, **kwargs)TP.predict(Xnew[, point, given, diag, model])TP.prior(name, X[, reparameterize, jitter])Return the TP prior distribution evaluated over the input locations X.
Attributes
Xfnu