TwoMoons#

class bayesflow.simulators.TwoMoons(lower_bound: float = -1.0, upper_bound: float = 1.0, rng: Generator = None)[source]#

Bases: BenchmarkSimulator

Two moons simulated benchmark. See: https://arxiv.org/pdf/2101.04653.pdf, Task T.8

Parameters:

lower_bound: float, optional, default: -1.0: The lower bound of the uniform prior
upper_bound: float, optional, default: 1.0: The upper bound of the uniform prior
rng: np.random.Generator or None, optional, default: None: An option random number generator to use

prior()[source]#

Generates a random draw from a 2-dimensional uniform prior bounded between lower_bound and upper_bound which represents the two parameters of the two moons simulator.

Returns:

params: np.ndarray of shape (2, ): A single draw from the 2-dimensional uniform prior.

__call__(**kwargs) → dict[str, ndarray]#: Call self as a function.

observation_model(params: ndarray)[source]#

Implements data generation from the two-moons model with a bimodal posterior.

Parameters:

params: np.ndarray of shape (2, ): The vector of two model parameters.

Returns:

observables: np.ndarray of shape (2, ): The 2D vector generated from the two moons simulator.

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

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

Runs simulated benchmark and returns batch_size parameter and observation batches

Parameters:

batch_shape: tuple: Number of parameter-observation batches to simulate.

Returns:

dict[str, np.ndarray]: simulated parameters and observables: with shapes (batch_size, …)