pymc.FullRank.set_size_and_deterministic

FullRank.set_size_and_deterministic(node, s, d, more_replacements=None)

Dev - after node is sampled via symbolic_sample_over_posterior() or symbolic_single_sample() new random generator can be allocated and applied to node

Parameters:

node: :class:`Variable`

PyTensor node with symbolically applied VI replacements

s: scalar

desired number of samples

d: bool or int

whether sampling is done deterministically

more_replacements: dict

more replacements to apply

Returns:

Variable with applied replacements, ready to use