pymc.gp.TP#

class pymc.gp.TP(*, mean_func=<pymc.gp.mean.Zero object>, cov_func=<pymc.gp.cov.Constant object>, 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
cov_funcNone, 2D array, or instance of Covariance

The covariance function. Defaults to zero.

mean_funcNone, instance of Mean

The mean function. Defaults to zero.

nufloat

The degrees of freedom

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, cov_func, nu])

TP.conditional(name, Xnew[, jitter])

Returns 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])

Returns the TP prior distribution evaluated over the input locations X.

Attributes

X

f

nu