# tfp.edward2.VectorDeterministic

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.