apply_bm

class pyprophet.ipf.apply_bm(data, use_log_space=False, evidence_epsilon=0.0)[source]

Bases:

Applies the Bayesian model to compute posterior probabilities.

Parameters:

data (pd.DataFrame) – Input Bayesian model data.
use_log_space (bool) – Whether to compute Bayesian posteriors in log-space.
evidence_epsilon (float) – Optional clipping epsilon applied to evidence values before inference. 0 disables clipping.

Returns:

Data with posterior probabilities for each hypothesis.

Return type:

pd.DataFrame