description: Particles with corresponding log weights.
View source on GitHub |
Particles with corresponding log weights.
tfp.experimental.mcmc.WeightedParticles(
particles, log_weights
)
This structure serves as the state
for the SequentialMonteCarlo
transition
kernel.
particles
: a (structure of) Tensor(s) each of shape
concat([[num_particles, b1, ..., bN], event_shape])
, where event_shape
may differ across component Tensor
s.log_weights
: float
Tensor
of shape
[num_particles, b1, ..., bN]
containing a log importance weight for
each particle, typically normalized so that
exp(reduce_logsumexp(log_weights, axis=0)) == 1.
. These must be used in
conjunction with particles
to compute expectations under the target
distribution.In some contexts, particles may be stacked across multiple inference steps,
in which case all Tensor
shapes will be prefixed by an additional dimension
of size num_steps
.
Attributes | |
---|---|
particles
|
|
log_weights
|