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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,
event_shape_tensors=None,
batch_shapes=None,
batch_shape_tensors=None,
dtypes=None,
shard_axis_names=None
)
Used in the notebooks
Used in the tutorials |
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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.
|
event_shape_tensors
|
A structure of tensors giving the (unconstrained)
event space shape of the desired samples. Must be an acceptable
input to constraining_bijector.inverse_event_shape_tensor . If
supplied together with model , acts as an override. Required if any of
event_shapes are not fully-defined.
|
batch_shapes
|
A structure of shapes giving the batch shape of the desired
samples. If supplied together with model , acts as an override. If
unspecified, we assume scalar batch [] .
|
batch_shape_tensors
|
A structure of tensors giving the batch shape of the
desired samples. If supplied together with model , acts as an override.
Required if any of batch_shapes are not fully-defined.
|
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.
|
shard_axis_names
|
A structure of str s indicating the named axes by which
the distribution event is sharded. See
tfp.experimental.distribute.Sharded for more context.
|
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]),
...)