tfp.experimental.mcmc.augment_with_state_history
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Decorates a transition or proposal fn to track state history.
tfp.experimental.mcmc.augment_with_state_history(
fn
)
For example usage, see
tfp.experimental.mcmc.augment_prior_with_state_history.
Args |
|---|
fn
|
Python callable to wrap, having signature
new_state_dist = fn(step, state_with_history, **kwargs) where
state_with_history is a StateWithHistory namedtuple.
|
Returns |
|---|
augmented_fn
|
Python callable wrapping fn, having signature
new_state_with_history_dist = augmented_fn(step, state_with_history,
**kwargs). The return value is a tfd.JointDistributionNamed instance
overtfp.experimental.mcmc.StateWithHistory namedtuples, in which the
state_history component is rotated to discard
the (previously-oldest) state at the initial position and append the
new state at the final position.
|
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Last updated 2023-11-21 UTC.
[null,null,["Last updated 2023-11-21 UTC."],[],[],null,["# tfp.experimental.mcmc.augment_with_state_history\n\n\u003cbr /\u003e\n\n|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/probability/blob/v0.23.0/tensorflow_probability/python/experimental/mcmc/particle_filter_augmentation.py#L87-L128) |\n\nDecorates a transition or proposal fn to track state history. \n\n tfp.experimental.mcmc.augment_with_state_history(\n fn\n )\n\nFor example usage, see\n[`tfp.experimental.mcmc.augment_prior_with_state_history`](../../../tfp/experimental/mcmc/augment_prior_with_state_history).\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `fn` | Python `callable` to wrap, having signature `new_state_dist = fn(step, state_with_history, **kwargs)` where `state_with_history` is a `StateWithHistory` namedtuple. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|----------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `augmented_fn` | Python `callable` wrapping `fn`, having signature `new_state_with_history_dist = augmented_fn(step, state_with_history, **kwargs)`. The return value is a `tfd.JointDistributionNamed` instance over[`tfp.experimental.mcmc.StateWithHistory`](../../../tfp/experimental/mcmc/StateWithHistory) namedtuples, in which the `state_history` component is rotated to discard the (previously-oldest) state at the initial position and append the new state at the final position. |\n\n\u003cbr /\u003e"]]
View source on GitHub