GaussianMixture

class bayesflow.simulators.GaussianMixture(D: int = 2, lower_bound: float = -10.0, upper_bound: float = 10.0, prob: float = 0.5, scale_c1: float = 1.0, scale_c2: float = 0.1, rng: Generator = None)

Bases: BenchmarkSimulator

Gaussian Mixture simulated benchmark See: https://arxiv.org/pdf/2101.04653.pdf, Task T.7

Important: The parameterization uses scales, so use sqrt(var), if you want to be working with variances instead of scales.

Parameters:

D: int, optional, default: 2

The dimensionality of the mixture model.

lower_bound: float, optional, default: -10.0

The lower bound of the uniform prior.

upper_bound: float, optional, default: 10.0

The upper bound of the uniform prior.

prob: float, optional, default: 0.5

The mixture probability (coefficient).

scale_c1: float, optional, default: 1.0

The scale of the first component.

scale_c2: float, optional, default: 0.1

The scale of the second component.

rng: np.random.Generator or None, optional, default: None

An optional random number generator to use.

prior()

Generates a random draw from a 2-dimensional uniform prior bounded between lower_bound and upper_bound representing the common mean of a 2D Gaussian mixture model (GMM).

Returns:

paramsnp.ndarray of shape (D, )

A single draw from the D-dimensional uniform prior

__call__(**kwargs) → dict[str, ndarray]

Call self as a function.

observation_model(params: ndarray)

Simulates data from the Gaussian mixture model (GMM) with shared location vector. For more details, see

Parameters:

paramsnp.ndarray of shape (D, )

The D-dimensional vector of parameter locations.

Returns:

xnp.ndarray of shape (2, )

The 2D vector generated from the GMM 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, …)