LambdaSimulator

class bayesflow.simulators.LambdaSimulator(sample_fn: Callable[[Sequence[int]], dict[str, any]], *, is_batched: bool = False)

Bases: Simulator

Implements a simulator based on a sampling function.

Initialize a simulator based on a simple callable function

Parameters:

sample_fnCallable[[Sequence[int]], dict[str, any]]

A function that generates samples. It should accept batch_shape as its first argument (if is_batched=True), followed by keyword arguments.

is_batchedbool, optional

Whether the sample_fn is implemented to handle batched sampling directly. If False, sample_fn will be called once per sample and results will be stacked. Default is False.

sample(batch_shape: tuple[int, ...], **kwargs) → dict[str, ndarray]

Sample using the wrapped sampling function.

Parameters:

batch_shapeShape

The shape of the batch to sample. Typically, a tuple indicating the number of samples, but an int can also be passed.

**kwargs

Additional keyword arguments passed to the sampling function. Only valid arguments (as determined by the function’s signature) are used.

Returns:

datadict of str to np.ndarray

A dictionary of sampled outputs. Keys are output names and values are numpy arrays. If is_batched is False, individual outputs are stacked along the first axis.

rejection_sample(batch_shape: tuple[int, ...], predicate: Callable[[dict[str, ndarray]], ndarray], *, axis: int = 0, sample_size: int = None, **kwargs) → dict[str, ndarray]