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tfp.edward2.VectorDiffeomixture

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Create a random variable for VectorDiffeomixture.

Aliases:

tfp.edward2.VectorDiffeomixture(
    *args,
    **kwargs
)

See VectorDiffeomixture for more details.

Returns:

RandomVariable.

Original Docstring for Distribution

Constructs the VectorDiffeomixture on R^d.

The vector diffeomixture (VDM) approximates the compound distribution

p(x) = int p(x | z) p(z) dz,
where z is in the K-simplex, and
p(x | z) := p(x | loc=sum_k z[k] loc[k], scale=sum_k z[k] scale[k])

Args:

  • mix_loc: float-like Tensor with shape [b1, ..., bB, K-1]. In terms of samples, larger mix_loc[..., k] ==> Z is more likely to put more weight on its kth component.
  • temperature: float-like Tensor. Broadcastable with mix_loc. In terms of samples, smaller temperature means one component is more likely to dominate. I.e., smaller temperature makes the VDM look more like a standard mixture of K components.
  • distribution: tfp.distributions.Distribution-like instance. Distribution from which d iid samples are used as input to the selected affine transformation. Must be a scalar-batch, scalar-event distribution. Typically distribution.reparameterization_type = FULLY_REPARAMETERIZED or it is a function of non-trainable parameters. WARNING: If you backprop through a VectorDiffeomixture sample and the distribution is not FULLY_REPARAMETERIZED yet is a function of trainable variables, then the gradient will be incorrect!
  • loc: Length-K list of float-type Tensors. The k-th element represents the shift used for the k-th affine transformation. If the k-th item is None, loc is implicitly 0. When specified, must have shape [B1, ..., Bb, d] where b >= 0 and d is the event size.
  • scale: Length-K list of LinearOperators. Each should be positive-definite and operate on a d-dimensional vector space. The k-th element represents the scale used for the k-th affine transformation. LinearOperators must have shape [B1, ..., Bb, d, d], b >= 0, i.e., characterizes b-batches of d x d matrices
  • quadrature_size: Python int scalar representing number of quadrature points. Larger quadrature_size means q_N(x) better approximates p(x).
  • quadrature_fn: Python callable taking normal_loc, normal_scale, quadrature_size, validate_args and returning tuple(grid, probs) representing the SoftmaxNormal grid and corresponding normalized weight. normalized) weight. Default value: quadrature_scheme_softmaxnormal_quantiles.
  • 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.

Raises:

  • ValueError: if not scale or len(scale) < 2.
  • ValueError: if len(loc) != len(scale)
  • ValueError: if quadrature_grid_and_probs is not None and len(quadrature_grid_and_probs[0]) != len(quadrature_grid_and_probs[1])
  • ValueError: if validate_args and any not scale.is_positive_definite.
  • TypeError: if any scale.dtype != scale[0].dtype.
  • TypeError: if any loc.dtype != scale[0].dtype.
  • NotImplementedError: if len(scale) != 2.
  • ValueError: if not distribution.is_scalar_batch.
  • ValueError: if not distribution.is_scalar_event.