bayesflow.benchmarks.gaussian_mixture module#
- bayesflow.benchmarks.gaussian_mixture.prior(lower_bound=-10.0, upper_bound=10.0, D=2, rng=None)[source]#
Generates a random draw from a 2-dimensional uniform prior bounded between lower_bound and upper_bound representing the common mean of a 2D Gaussian mixture model (GMM).
- Parameters:
- lower_boundfloat, optional, default-10
The lower bound of the uniform prior
- upper_boundfloat, optional, default10
The upper bound of the uniform prior
- Dint, optional, default: 2
The dimensionality of the mixture model
- 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_mixture.simulator(theta, prob=0.5, scale_c1=1.0, scale_c2=0.1, rng=None)[source]#
Simulates data from the Gaussian mixture model (GMM) with shared location vector. For more details, see
https://arxiv.org/pdf/2101.04653.pdf, Benchmark Task T.7
Important: The parameterization uses scales, so use sqrt(var), if you want to be working with variances instead of scales.
- Parameters:
- thetanp.ndarray of shape (D,)
The D-dimensional vector of parameter locations.
- probfloat, optional, default: 0.5
The mixture probability (coefficient).
- scale_c1float, optional, default: 1.
The scale of the first component
- scale_c2float, optional, default: 0.1
The scale of the second component
- rngnp.random.Generator or None, default: None
An optional random number generator to use
- Returns:
- xnp.ndarray of shape (2,)
The 2D vector generated from the GMM simulator.