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Returns an initialization Distribution for starting a Markov chain.
tfp.experimental.mcmc.init_near_unconstrained_zero(
model=None, constraining_bijector=None, event_shapes=None, dtypes=None
)
This initialization scheme follows Stan: we sample every latent
independently, uniformly from -2 to 2 in its unconstrained space,
and then transform into constrained space to construct an initial
state that can be passed to sample_chain
or other MCMC drivers.
The argument signature is arranged to let the user pass either a
JointDistribution
describing their model, if it's in that form, or
the essential information necessary for the sampling, namely a
bijector (from unconstrained to constrained space) and the desired
shape and dtype of each sample (specified in constrained space).
Args | |
---|---|
model
|
A Distribution (typically a JointDistribution ) giving the
model to be initialized. If supplied, it is queried for
its default event space bijector, its event shape, and its dtype.
If not supplied, those three elements must be supplied instead.
|
constraining_bijector
|
A (typically multipart) Bijector giving
the mapping from unconstrained to constrained space. If
supplied together with a model , acts as an override. A nested
structure of Bijector s is accepted, and interpreted as
applying in parallel to a corresponding structure of state parts
(see JointMap for details).
|
event_shapes
|
A structure of shapes giving the (unconstrained)
event space shape of the desired samples. Must be an acceptable
input to constraining_bijector.inverse_event_shape . If
supplied together with model , acts as an override.
|
dtypes
|
A structure of dtypes giving the (unconstrained) dtypes of
the desired samples. Must be an acceptable input to
constraining_bijector.inverse_dtype . If supplied together
with model , acts as an override.
|
Returns | |
---|---|
init_dist
|
A Distribution representing the initialization
distribution, in constrained space. Samples from this
Distribution are valid initial states for a Markov chain
targeting the model.
|
Example
Initialize 100 chains from the unconstrained -2, 2 distribution
for a model expressed as a JointDistributionCoroutine
:
@tfp.distributions.JointDistributionCoroutine
def model():
...
init_dist = tfp.experimental.mcmc.init_near_unconstrained_zero(model)
states = tfp.mcmc.sample_chain(
current_state=init_dist.sample(100, seed=[4, 8]),
...)