BernoulliGLM#
- class bayesflow.simulators.BernoulliGLM(T: int = 100, scale_by_T: bool = True, rng: Generator = None)[source]#
Bases:
BenchmarkSimulatorBernoulli 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()[source]#
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.
- observation_model(params: ndarray)[source]#
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, …)
- 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.