bayesflow.benchmarks.bernoulli_glm module#
- bayesflow.benchmarks.bernoulli_glm.prior(rng=None)[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.
- Parameters:
- rngnp.random.Generator or None, default: None
An optional random number generator to use.
- Returns:
- thetanp.ndarray of shape (10,)
A single draw from the prior.
- bayesflow.benchmarks.bernoulli_glm.simulator(theta, T=100, scale_by_T=True, rng=None)[source]#
Simulates data from the custom Bernoulli GLM likelihood, 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:
- thetanp.ndarray of shape (10,)
The vector of model parameters (theta[0] is intercept, theta[i], i > 0 are weights).
- Tint, optional, default: 100
The simulated duration of the task (eq. the number of Bernoulli draws).
- scale_by_Tbool, optional, default: True
A flag indicating whether to scale the summayr statistics by T.
- rngnp.random.Generator or None, default: None
An optional random number generator to use.
- Returns:
- xnp.ndarray of shape (10,)
The vector of sufficient summary statistics of the data.