class_docstr: A model augmented with parameter perturbations for iterated filtering.
class_docstr_tok: ["A","model","augmented","with","parameter","perturbations","for","iterated","filtering","."]
docstr: Proposal to initialize the model with given parameter particles.
docstr_tok: ["Proposal","to","initialize","the","model","with","given","parameter","particles","."]
code: def joint_initial_state_proposal(
      self, initial_unconstrained_parameters=None
  ):
      """Proposal to initialize the model with given parameter particles."""
      if initial_unconstrained_parameters is None:
          joint_initial_state_proposal = (
              self._joint_initial_state_proposal
          )
      else:
          # Hack: DeterministicEmpirical is a fake distribution whose `sample`
          # just proposes *exactly* the parameters we pass in.
          unconstrained_parameter_proposal = DeterministicEmpirical(
              initial_unconstrained_parameters,
              batch_ndims=self.batch_ndims,
          )
  
          # Propose initial state conditioned on the parameters.
          joint_initial_state_proposal = (
              joint_prior_on_parameters_and_state(
                  unconstrained_parameter_proposal,
                  self.parameterized_initial_state_proposal_fn,
                  parameter_constraining_bijector=(
                      self.parameter_constraining_bijector
                  ),
                  prior_is_constrained=False,
              )
          )
  
      # May return `None` if no initial proposal or params were specified.
      return joint_initial_state_proposal
  
imports: import collections
  import contextlib
  import functools
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import invert
  from tensorflow_probability.python.distributions import distribution
  from tensorflow_probability.python.distributions import independent
  from tensorflow_probability.python.distributions import joint_distribution_named
  from tensorflow_probability.python.distributions import joint_distribution_sequential
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import transformed_distribution
  from tensorflow_probability.python.experimental.mcmc.particle_filter import infer_trajectories
  from tensorflow_probability.python.internal import distribution_util
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import reparameterization
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import tensorshape_util
label_desc: Use image node_id 4 for calling the IteratedFilter obj's underlying member method code with example usage: obj.joint_initial_state_proposal(initial_unconstrained_parameters) and returns: joint_initial_state_proposal