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Create a random variable for OrderedLogistic.

    *args, **kwargs

See OrderedLogistic for more details.



Original Docstring for Distribution

Initialize Ordered Logistic distributions.


  • cutpoints: A floating-point Tensor with shape [B1, ..., Bb, K] where b >= 0 indicates the number of batch dimensions. Each entry is then a K-length vector of cutpoints. The vector of cutpoints should be non-decreasing, which is only checked if validate_args=True.
  • loc: A floating-point Tensor with shape [B1, ..., Bb] where b >= 0 indicates the number of batch dimensions. The entries represent the mean(s) of the latent logistic distribution(s). Different batch shapes for cutpoints and loc are permitted, with the distribution batch_shape being tf.shape(loc[..., tf.newaxis] - cutpoints)[:-1] assuming the subtraction is a valid broadcasting operation.
  • dtype: The type of the event samples (default: int32).
  • 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.
  • allow_nan_stats: Python bool, default True. When True, statistics (e.g. mode) 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.
  • name: Python str name prefixed to Ops created by this class.