bayesflow.benchmarks.slcp module#
- bayesflow.benchmarks.slcp.prior(lower_bound=-3.0, upper_bound=3.0, rng=None)[source]#
Generates a random draw from a 5-dimensional uniform prior bounded between lower_bound and upper_bound which represents the 5 parameters of the SLCP simulator.
- Parameters:
- lower_boundfloat, optional, default-3
The lower bound of the uniform prior.
- upper_boundfloat, optional, default3
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 (5, )
A single draw from the 5-dimensional uniform prior.
- bayesflow.benchmarks.slcp.simulator(theta, n_obs=4, flatten=True, rng=None)[source]#
Generates data from the SLCP model designed as a benchmark for a simple likelihood and a complex posterior due to a non-linear pushforward theta -> x.
See https://arxiv.org/pdf/2101.04653.pdf, Benchmark Task T.3
- Parameters:
- thetanp.ndarray of shape (theta, D)
The location parameters of the Gaussian likelihood.
- n_obsint, optional, default: 4
The number of observations to generate from the slcp likelihood.
- flattenbool, optional, default: True
A flag to indicate whather a 1D (flatten=True) or a 2D (flatten=False) representation of the simulated data is returned.
- rngnp.random.Generator or None, default: None
An optional random number generator to use.
- Returns:
- xnp.ndarray of shape (n_obs*2, ) or (n_obs, 2), as indictated by the flatten
boolean flag. The sample of simulated data from the SLCP model.