BernoulliGLMRaw

class bayesflow.simulators.BernoulliGLMRaw(T: int = 100, rng: Generator = None)

Bases: BenchmarkSimulator

Bernoulli GLM raw simulated benchmark. See: https://arxiv.org/pdf/2101.04653.pdf, Task T.6

Parameters:

T: int, optional, default: 100

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

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:

paramsnp.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 and returns the raw Bernoulli data.

Parameters:

paramsnp.ndarray of shape (10, )

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

Returns:

xnp.ndarray of shape (T, 10)

The full simulated set of Bernoulli draws and design matrix. Should be configured with an additional trailing dimension if the data is (properly) to be treated as a set.

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, …)