BernoulliGLM

class bayesflow.simulators.BernoulliGLM(T: int = 100, scale_by_T: bool = True, rng: Generator = None)

Bases: BenchmarkSimulator

Bernoulli GLM simulated benchmark See: https://arxiv.org/pdf/2101.04653.pdf, Task T.5

Important: scale_sum should be set to False if the simulator is used with variable T during training, otherwise the information of T will be lost.

Parameters:

T: int, optional, default: 100

The simulated duration of the task (eq. the number of Bernoulli draws).

scale_by_T: bool, optional, default: True

A flag indicating whether to scale the summayr statistics by T.

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

An optional random number generator to use.

prior()

Generates a random draw from the custom prior over the 10 Bernoulli GLM parameters (1 intercept and 9 weights). Uses a global covariance matrix Cov for the multivariate Gaussian prior over the model weights, which is pre-computed for efficiency.

Returns:

params: np.ndarray of shape (10, )

A single draw from the prior.

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

Call self as a function.

observation_model(params: ndarray)

Simulates data from the custom Bernoulli GLM likelihood.

Parameters:

params: np.ndarray of shape (10, )

The vector of model parameters (params[0] is intercept, params[i], i > 0 are weights).

Returns:

x: np.ndarray of shape (10, )

The vector of sufficient summary statistics of the data.

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, …)