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
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).
- Parameters:
mean_mu_G0 (float)
sigma_mu_G0 (float)
sigma_pi_G0 (float)
key (Array)
n_samples (int)
- Return type:
Tuple[Array, Array, Array]