def conditional(self, name, Xnew, jitter=0.0, **kwargs):
R"""
Returns the conditional distribution evaluated over new input
locations `Xnew`.
Given a set of function values `f` that
the TP prior was over, the conditional distribution over a
set of new points, `f_*` is
Parameters
----------
name: string
Name of the random variable
Xnew: array-like
Function input values.
jitter: scalar
A small correction added to the diagonal of positive semi-definite
covariance matrices to ensure numerical stability. For conditionals
the default value is 0.0.
**kwargs
Extra keyword arguments that are passed to `MvNormal` distribution
constructor.
"""
X = self.X
f = self.f
nu2, mu, cov = self._build_conditional(Xnew, X, f, jitter)
return pm.MvStudentT(name, nu=nu2, mu=mu, cov=cov, **kwargs)
def conditional(self, name, Xnew, **kwargs):
R"""
Returns the conditional distribution evaluated over new input
locations `Xnew`.
Given a set of function values `f` that
the TP prior was over, the conditional distribution over a
set of new points, `f_*` is
Parameters
----------
name: string
Name of the random variable
Xnew: array-like
Function input values.
**kwargs
Extra keyword arguments that are passed to `MvNormal` distribution
constructor.
"""
X = self.X
f = self.f
nu2, mu, cov = self._build_conditional(Xnew, X, f)
shape = infer_shape(Xnew, kwargs.pop("shape", None))
return pm.MvStudentT(name, nu=nu2, mu=mu, cov=cov, size=shape, **kwargs)
def _build_prior(self, name, X, reparameterize=True, jitter=JITTER_DEFAULT, **kwargs):
mu = self.mean_func(X)
cov = stabilize(self.cov_func(X), jitter)
if reparameterize:
size = infer_size(X, kwargs.pop("size", None))
v = pm.StudentT(name + "_rotated_", mu=0.0, sigma=1.0, nu=self.nu, size=size, **kwargs)
f = pm.Deterministic(name, mu + cholesky(cov).dot(v))
else:
f = pm.MvStudentT(name, nu=self.nu, mu=mu, cov=cov, **kwargs)
return f
def _build_prior(self, name, X, reparameterize=True, **kwargs):
mu = self.mean_func(X)
cov = stabilize(self.cov_func(X))
shape = infer_shape(X, kwargs.pop("shape", None))
if reparameterize:
chi2 = pm.ChiSquared(name + "_chi2_", self.nu)
v = pm.Normal(name + "_rotated_", mu=0.0, sigma=1.0, size=shape, **kwargs)
f = pm.Deterministic(name, (at.sqrt(self.nu) / chi2) * (mu + cholesky(cov).dot(v)))
else:
f = pm.MvStudentT(name, nu=self.nu, mu=mu, cov=cov, size=shape, **kwargs)
return f
