pyhgf.updates.prediction_error.dirichlet.likely_cluster_proposal#
- pyhgf.updates.prediction_error.dirichlet.likely_cluster_proposal(mean_mu_G0, sigma_mu_G0, sigma_pi_G0, 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=Array((), dtype=key<fry>) overlaying: [ 0 42], n_samples=20000)[source]#
Sample likely new belief distributions given pre-existing clusters.
- Parameters:
mean_mu_G0 (float) – The mean of the mean of the base distribution.
sigma_mu_G0 (float) – The standard deviation of mean of the base distribution.
sigma_pi_G0 (float) – 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 (Array) – Random state.
n_samples (int) – 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).
- Return type:
tuple[Array, Array, Array]