Create a random variable for StudentTProcess.

See StudentTProcess for more details.



Original Docstring for Distribution

Instantiate a StudentTProcess Distribution.


  • df: Positive Floating-point Tensor representing the degrees of freedom. Must be greater than 2.
  • kernel: PositiveSemidefiniteKernel-like instance representing the TP's covariance function.
  • index_points: float Tensor representing finite (batch of) vector(s) of points in the index set over which the TP is defined. Shape has the form [b1, ..., bB, e, f1, ..., fF] where F is the number of feature dimensions and must equal kernel.feature_ndims and e is the number (size) of index points in each batch. Ultimately this distribution corresponds to a e-dimensional multivariate Student's T. The batch shape must be broadcastable with kernel.batch_shape and any batch dims yielded by mean_fn.
  • mean_fn: Python callable that acts on index_points to produce a (batch of) vector(s) of mean values at index_points. Takes a Tensor of shape [b1, ..., bB, f1, ..., fF] and returns a Tensor whose shape is broadcastable with [b1, ..., bB]. Default value: None implies constant zero function.
  • jitter: float scalar Tensor added to the diagonal of the covariance matrix to ensure positive definiteness of the covariance matrix. Default value: 1e-6.
  • validate_args: Python bool, default False. When True distribution parameters are checked for validity despite possibly degrading runtime performance. When False invalid inputs may silently render incorrect outputs. Default value: False.
  • allow_nan_stats: Python bool, default True. When 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: False.
  • name: Python str name prefixed to Ops created by this class. Default value: "StudentTProcess".


  • ValueError: if mean_fn is not None and is not callable.