|---| | [ View source on GitHub](https://github.com/tensorflow/probability/blob/v0.25.0/tensorflow_probability/python/mcmc/langevin.py#L317-L578) | Runs one step of Uncalibrated Langevin discretized diffusion. Inherits From: [`TransitionKernel`](https://www.tensorflow.org/probability/api_docs/python/tfp/mcmc/TransitionKernel) tfp.mcmc.UncalibratedLangevin( target_log_prob_fn, step_size, volatility_fn=None, parallel_iterations=10, compute_acceptance=True, experimental_shard_axis_names=None, name=None ) The class generates a Langevin proposal using `_euler_method` function and also computes helper `UncalibratedLangevinKernelResults` for the next iteration. > [!WARNING] > **Warning:** this kernel will not result in a chain which converges to the `target_log_prob`. To get a convergent MCMC, use `MetropolisAdjustedLangevinAlgorithm(...)` or `MetropolisHastings(UncalibratedLangevin(...))`. For more details on `UncalibratedLangevin`, see `MetropolisAdjustedLangevinAlgorithm`.


| ## Args || |---|---| | `target_log_prob_fn` | Python callable which takes an argument like `current_state` (or `*current_state` if it's a list) and returns its (possibly unnormalized) log-density under the target distribution. | | `step_size` | `Tensor` or Python `list` of `Tensor`s representing the step size for the leapfrog integrator. Must broadcast with the shape of `current_state`. Larger step sizes lead to faster progress, but too-large step sizes make rejection exponentially more likely. When possible, it's often helpful to match per-variable step sizes to the standard deviations of the target distribution in each variable. | | `volatility_fn` | Python callable which takes an argument like `current_state` (or `*current_state` if it's a list) and returns volatility value at `current_state`. Should return a `Tensor` or Python `list` of `Tensor`s that must broadcast with the shape of `current_state` Defaults to the identity function. | | `parallel_iterations` | the number of coordinates for which the gradients of the volatility matrix `volatility_fn` can be computed in parallel. | | `compute_acceptance` | Python 'bool' indicating whether to compute the Metropolis log-acceptance ratio used to construct `MetropolisAdjustedLangevinAlgorithm` kernel. | | `experimental_shard_axis_names` | A structure of string names indicating how members of the state are sharded. | | `name` | Python `str` name prefixed to Ops created by this function. Default value: `None` (i.e., 'mala_kernel'). |



| ## Raises || |---|---| | `ValueError` | if there isn't one `step_size` or a list with same length as `current_state`. | | `TypeError` | if `volatility_fn` is not callable. |



| ## Attributes || |---|---| | `compute_acceptance` |

| | `experimental_shard_axis_names` | The shard axis names for members of the state. | | `is_calibrated` | Returns `True` if Markov chain converges to specified distribution.
`TransitionKernel`s which are "uncalibrated" are often calibrated by composing them with the [`tfp.mcmc.MetropolisHastings`](https://www.tensorflow.org/probability/api_docs/python/tfp/mcmc/MetropolisHastings) `TransitionKernel`. | | `name` |

| | `parallel_iterations` |

| | `parameters` | Return `dict` of `__init__` arguments and their values. | | `step_size` |

| | `target_log_prob_fn` |

| | `volatility_fn` |

|
## Methods ### `bootstrap_results` [View source](https://github.com/tensorflow/probability/blob/v0.25.0/tensorflow_probability/python/mcmc/langevin.py#L529-L571) bootstrap_results( init_state ) Creates initial `previous_kernel_results` using a supplied `state`. ### `copy` [View source](https://github.com/tensorflow/probability/blob/v0.25.0/tensorflow_probability/python/mcmc/kernel.py#L148-L164) copy( **override_parameter_kwargs ) Non-destructively creates a deep copy of the kernel.


| Args || |---|---| | `**override_parameter_kwargs` | Python String/value `dictionary` of initialization arguments to override with new values. |



| Returns || |---|---| | `new_kernel` | `TransitionKernel` object of same type as `self`, initialized with the union of self.parameters and override_parameter_kwargs, with any shared keys overridden by the value of override_parameter_kwargs, i.e., `dict(self.parameters, **override_parameters_kwargs)`. |
### `experimental_with_shard_axes` [View source](https://github.com/tensorflow/probability/blob/v0.25.0/tensorflow_probability/python/mcmc/langevin.py#L577-L578) experimental_with_shard_axes( shard_axis_names ) Returns a copy of the kernel with the provided shard axis names.


| Args || |---|---| | `shard_axis_names` | a structure of strings indicating the shard axis names for each component of this kernel's state. |



| Returns || |---|---| | A copy of the current kernel with the shard axis information. ||
### `one_step` [View source](https://github.com/tensorflow/probability/blob/v0.25.0/tensorflow_probability/python/mcmc/langevin.py#L428-L527) one_step( current_state, previous_kernel_results, seed=None ) Runs one iteration of MALA.


| Args || |---|---| | `current_state` | `Tensor` or Python `list` of `Tensor`s representing the current state(s) of the Markov chain(s). The first `r` dimensions index independent chains, `r = tf.rank(target_log_prob_fn(*current_state))`. | | `previous_kernel_results` | `collections.namedtuple` containing `Tensor`s representing values from previous calls to this function (or from the `bootstrap_results` function.) | | `seed` | PRNG seed; see [`tfp.random.sanitize_seed`](https://www.tensorflow.org/probability/api_docs/python/tfp/random/sanitize_seed) for details. |



| Returns || |---|---| | `next_state` | Tensor or Python list of `Tensor`s representing the state(s) of the Markov chain(s) after taking exactly one step. Has same type and shape as `current_state`. | | `kernel_results` | `collections.namedtuple` of internal calculations used to advance the chain. |



| Raises || |---|---| | `ValueError` | if there isn't one `step_size` or a list with same length as `current_state` or `diffusion_drift`. |