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__init__.py
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__init__.py
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# Copyright 2018 The TensorFlow Probability Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""TensorFlow Probability MCMC python package."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow_probability.python.mcmc.diagnostic import effective_sample_size
from tensorflow_probability.python.mcmc.diagnostic import potential_scale_reduction
from tensorflow_probability.python.mcmc.dual_averaging_step_size_adaptation import DualAveragingStepSizeAdaptation
from tensorflow_probability.python.mcmc.hmc import HamiltonianMonteCarlo
from tensorflow_probability.python.mcmc.hmc import make_simple_step_size_update_policy
from tensorflow_probability.python.mcmc.hmc import UncalibratedHamiltonianMonteCarlo
from tensorflow_probability.python.mcmc.kernel import TransitionKernel
from tensorflow_probability.python.mcmc.langevin import MetropolisAdjustedLangevinAlgorithm
from tensorflow_probability.python.mcmc.langevin import UncalibratedLangevin
from tensorflow_probability.python.mcmc.metropolis_hastings import MetropolisHastings
from tensorflow_probability.python.mcmc.nuts import NoUTurnSampler
from tensorflow_probability.python.mcmc.random_walk_metropolis import random_walk_normal_fn
from tensorflow_probability.python.mcmc.random_walk_metropolis import random_walk_uniform_fn
from tensorflow_probability.python.mcmc.random_walk_metropolis import RandomWalkMetropolis
from tensorflow_probability.python.mcmc.random_walk_metropolis import UncalibratedRandomWalk
from tensorflow_probability.python.mcmc.replica_exchange_mc import default_swap_proposal_fn
from tensorflow_probability.python.mcmc.replica_exchange_mc import ReplicaExchangeMC
from tensorflow_probability.python.mcmc.sample import CheckpointableStatesAndTrace
from tensorflow_probability.python.mcmc.sample import sample_chain
from tensorflow_probability.python.mcmc.sample import StatesAndTrace
from tensorflow_probability.python.mcmc.sample_annealed_importance import sample_annealed_importance_chain
from tensorflow_probability.python.mcmc.sample_halton_sequence import sample_halton_sequence
from tensorflow_probability.python.mcmc.simple_step_size_adaptation import SimpleStepSizeAdaptation
from tensorflow_probability.python.mcmc.slice_sampler_kernel import SliceSampler
from tensorflow_probability.python.mcmc.transformed_kernel import TransformedTransitionKernel
__all__ = [
'CheckpointableStatesAndTrace',
'DualAveragingStepSizeAdaptation',
'HamiltonianMonteCarlo',
'MetropolisAdjustedLangevinAlgorithm',
'MetropolisHastings',
'NoUTurnSampler',
'RandomWalkMetropolis',
'ReplicaExchangeMC',
'SimpleStepSizeAdaptation',
'SliceSampler',
'StatesAndTrace',
'TransformedTransitionKernel',
'TransitionKernel',
'UncalibratedHamiltonianMonteCarlo',
'UncalibratedLangevin',
'UncalibratedRandomWalk',
'default_swap_proposal_fn',
'effective_sample_size',
'make_simple_step_size_update_policy',
'potential_scale_reduction',
'random_walk_normal_fn',
'random_walk_uniform_fn',
'sample_annealed_importance_chain',
'sample_chain',
'sample_halton_sequence',
]