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, …)

sample_batched(batch_shape: tuple[int, ...], *, sample_size: int, **kwargs)

Sample the desired number of simulations in smaller batches.

Limited resources, especially memory, can make it necessary to run simulations in smaller batches. The number of samples per simulated batch is specified by sample_size.

Parameters:

batch_shapeShape

The desired output shape, as in sample(). Will be rounded up to the next complete batch.

sample_sizeint

The number of samples in each simulated batch.

kwargs

Additional keyword arguments passed to sample().