Create a random variable for GaussianProcess.
tfp.edward2.GaussianProcess( *args, **kwargs )
See GaussianProcess for more details.
Original Docstring for Distribution
Instantiate a GaussianProcess Distribution.
PositiveSemidefiniteKernel-like instance representing the GP's covariance function.
Tensorrepresenting finite (batch of) vector(s) of points in the index set over which the GP is defined. Shape has the form
[b1, ..., bB, e, f1, ..., fF]where
Fis the number of feature dimensions and must equal
eis the number (size) of index points in each batch. Ultimately this distribution corresponds to a
e-dimensional multivariate normal. The batch shape must be broadcastable with
kernel.batch_shapeand any batch dims yielded by
callablethat acts on
index_pointsto produce a (batch of) vector(s) of mean values at
index_points. Takes a
[b1, ..., bB, f1, ..., fF]and returns a
Tensorwhose shape is broadcastable with
[b1, ..., bB]. Default value:
Noneimplies constant zero function.
Tensorrepresenting the variance of the noise in the Normal likelihood distribution of the model. May be batched, in which case the batch shape must be broadcastable with the shapes of all other batched parameters (
index_points, etc.). Default value:
Tensoradded to the diagonal of the covariance matrix to ensure positive definiteness of the covariance matrix. Default value:
Truedistribution parameters are checked for validity despite possibly degrading runtime performance. When
Falseinvalid inputs may silently render incorrect outputs. Default value:
True, statistics (e.g., mean, mode, variance) use the value "
NaN" to indicate the result is undefined. When
False, an exception is raised if one or more of the statistic's batch members are undefined. Default value:
strname prefixed to Ops created by this class. Default value: "GaussianProcess".
Noneand is not callable.