BassModel.fit#

BassModel.fit(data, progressbar=None, random_seed=None, **kwargs)[source]#

Fit the Bass diffusion model via MCMC.

Parameters:

dataxr.Dataset, pd.DataFrame, pd.Series, np.ndarray: Adoption counts over time. See to_bass_dataset() for formats.
progressbarbool, optional: Whether to show the progress bar. Defaults to True.
random_seedoptional: Random seed for reproducibility.
**kwargs: Additional arguments forwarded to pymc.sample().

Returns:

arviz.InferenceData: Posterior with parameters and deterministics (adopters, innovators, imitators, peak) plus a fit_data group.

Notes

After fitting, use standard ArviZ functions for posterior analysis:

import arviz as az

# Parameter summaries
az.summary(idata, var_names=["m", "p", "q"])

# Trace plots
az.plot_trace(idata, var_names=["m", "p", "q"])

# Forest plots of peak adoption time
az.plot_forest(idata.posterior["peak"], combined=True)

For posterior predictive sampling with new time points:

pp = model.sample_posterior_predictive(X=new_data)