pyhgf.updates.prediction_error.nodes.dirichlet.likely_cluster_proposal

pyhgf.updates.prediction_error.nodes.dirichlet.likely_cluster_proposal(mean_mu_G0: float, sigma_mu_G0: float, sigma_pi_G0: float, expected_mean=typing.Union[jax.Array, numpy.ndarray, numpy.bool_, numpy.number, bool, int, float, complex], expected_sigma=typing.Union[jax.Array, numpy.ndarray, numpy.bool_, numpy.number, bool, int, float, complex], key: ~jax.Array = Array((), dtype=key<fry>) overlaying: [ 0 42], n_samples: int = 20000) → Tuple[Array, Array, Array]

Sample likely new belief distributions given pre-existing clusters.

Parameters:

mean_mu_G0

The mean of the mean of the base distribution.

sigma_mu_G0

The standard deviation of mean of the base distribution.

sigma_pi_G0

The standard deviation of the standard deviation of the base distribution.

expected_mean

Pre-existing clusters means.

expected_sigma

Pre-existing clusters standard deviation.

key

Random state.

n_samples

The number of samples used during the simulations.

Returns:

new_mu

A vector of means candidates.

new_sigma

A vector of standard deviation candidates.

weights

Weigths for each cluster candidate under pre-existing cluster (irrespective of new observations).