bayesflow.benchmarks.gaussian_linear module#
- bayesflow.benchmarks.gaussian_linear.prior(D=10, scale=0.1, rng=None)[source]#
Generates a random draw from a D-dimensional Gaussian prior distribution with a spherical scale matrix given by sigma * I_D. Represents the location vector of a (conjugate) Gaussian likelihood.
- Parameters:
- Dint, optional, default10
The dimensionality of the Gaussian prior distribution.
- scalefloat, optional, default0.1
The scale of the Gaussian prior.
- rngnp.random.Generator or None, default: None
An optional random number generator to use.
- Returns:
- thetanp.ndarray of shape (D, )
A single draw from the D-dimensional Gaussian prior.
- bayesflow.benchmarks.gaussian_linear.simulator(theta, n_obs=None, scale=0.1, rng=None)[source]#
Generates batched draws from a D-dimenional Gaussian distributions given a batch of location (mean) parameters of D dimensions. Assumes a spherical convariance matrix given by scale * I_D.
- Parameters:
- thetanp.ndarray of shape (theta, D)
The location parameters of the Gaussian likelihood.
- n_obsint or None, optional, default: None
The number of observations to draw from the likelihood given the location parameter theta. If n obs is None, a single draw is produced.
- scalefloat, optional, default0.1
The scale of the Gaussian likelihood.
- rngnp.random.Generator or None, default: None
An optional random number generator to use.
- Returns:
- xnp.ndarray of shape (theta.shape[0], theta.shape[1]) if n_obs is None,
else np.ndarray of shape (theta.shape[0], n_obs, theta.shape[1]) A single draw or a sample from a batch of Gaussians.