ImportanceSamplingPosteriorParameters

class ImportanceSamplingPosteriorParameters(theta_transform=None, method='sir', oversampling_factor=32, max_sampling_batch_size=10000)

Bases: PosteriorParameters

Parameters for initializing ImportanceSamplingPosterior.

Fields:

theta_transform: Transformation that is applied to parameters. Is not used

during but only when calling .map().

method: Either of [sir`|`importance]. This sets the behavior of the

.sample() method. With sir, approximate posterior samples are generated with sampling importance resampling (SIR). With importance, the .sample() method returns a tuple of samples and corresponding importance weights.

oversampling_factor: Number of proposed samples from which only one is

selected based on its importance weight.

max_sampling_batch_size: The batch size of samples being drawn from the

proposal at every iteration.

Parameters:

• theta_transform (Transform | None)

• method (Literal['sir', 'importance'])

• oversampling_factor (int)

• max_sampling_batch_size (int)

theta_transform: Transform | None = None

method: Literal['sir', 'importance'] = 'sir'

oversampling_factor: int = 32

max_sampling_batch_size: int = 10000

validate()

Validate ImportanceSamplingPosteriorParameters fields.

with_param(**kwargs)

Create a new instance of the class with updated field values.

Only allows updates to fields defined in the dataclass. Raises an error if any unknown or invalid field names are passed.

Parameters:

**kwargs – Field-value pairs to override in the new instance.

Returns:

A new instance of the same class with updated values.

Raises:

ValueError – If any of the provided keys are not valid dataclass fields.