bayesflow.benchmarks.gaussian_linear_uniform module#
- bayesflow.benchmarks.gaussian_linear_uniform.prior(D=10, lower_bound=-1.0, upper_bound=1.0, rng=None)[source]#
Generates a random draw from a D-dimensional uniform prior bounded between lower_bound and upper_bound which represents the location vector of a (conjugate) Gaussian likelihood.
- Parameters:
- Dint, optional, default10
The dimensionality of the Gaussian prior.
- lower_boundfloat, optional, default-1.
The lower bound of the uniform prior.
- upper_boundfloat, optional, default1.
The upper bound of the uniform 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 uniform prior.
- bayesflow.benchmarks.gaussian_linear_uniform.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 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.