tfp.edward2.VectorDeterministic

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Create a random variable for VectorDeterministic.

See VectorDeterministic for more details.

RandomVariable.

Original Docstring for Distribution

Initialize a VectorDeterministic distribution on R^k, for k >= 0.

Note that there is only one point in R^0, the 'point' []. So if k = 0 then self.prob([]) == 1.

The atol and rtol parameters allow for some slack in pmf computations, e.g. due to floating-point error.

pmf(x; loc)
  = 1, if All[Abs(x - loc) <= atol + rtol * Abs(loc)],
  = 0, otherwise

loc Numeric Tensor of shape [B1, ..., Bb, k], with b >= 0, k >= 0 The point (or batch of points) on which this distribution is supported.
atol Non-negative Tensor of same dtype as loc and broadcastable shape. The absolute tolerance for comparing closeness to loc. Default is 0.
rtol Non-negative Tensor of same dtype as loc and broadcastable shape. The relative tolerance for comparing closeness to loc. Default is 0.
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., 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.
name Python str name prefixed to Ops created by this class.