/model-fitting/index.html This section covers the core fitting workflow for PanelMMM: running MCMC, checking priors before fitting, and saving a fitted model for later reuse. Pages Fitting the Model - How fit() works, how sampler settings are applied, and what you get back. Prior Predictive Checks - How to sample and inspect prior predictive draws before fitting. Save and Load - How to persist a fitted model to NetCDF and rebuild PanelMMM from saved InferenceData. Hugo en-gb /model-fitting/fitting-the-model/index.html Mon, 01 Jan 0001 00:00:00 +0000 /model-fitting/fitting-the-model/index.html Use this page after you have prepared X and y for PanelMMM. For input requirements, see Data Preparation. Basic workflow fit() is the main entry point for posterior sampling. import pandas as pd from abacus.mmm import GeometricAdstock, LogisticSaturation from abacus.mmm.panel import PanelMMM dataset = pd.read_csv("data/demo/timeseries/dataset.csv") dataset["date"] = pd.to_datetime(dataset["date"]) X = dataset.drop(columns=["revenue"]) y = dataset["revenue"].rename("revenue") mmm = PanelMMM( date_column="date", target_column="revenue", channel_columns=[ "channel_1", "channel_2", "channel_3", "channel_4", "channel_5", "channel_6", ], yearly_seasonality=2, adstock=GeometricAdstock(l_max=4), saturation=LogisticSaturation(), ) idata = mmm.fit( X, y, draws=500, tune=500, chains=2, cores=2, progressbar=False, random_seed=42, ) fit() returns an arviz.InferenceData object and also stores it on mmm.idata. /model-fitting/prior-predictive-checks/index.html Mon, 01 Jan 0001 00:00:00 +0000 /model-fitting/prior-predictive-checks/index.html Run prior predictive checks before fitting when you want to test whether your configured priors imply plausible target behaviour. If you want the econometrics framing for this workflow, see Prior Predictive Checks for Econometricians. Sample prior predictive draws Use sample_prior_predictive(...) on PanelMMM: prior = mmm.sample_prior_predictive( X=X, y=y, samples=100, random_seed=42, ) In normal PanelMMM use, pass the same X and y structure that you plan to fit. /model-fitting/save-and-load/index.html Mon, 01 Jan 0001 00:00:00 +0000 /model-fitting/save-and-load/index.html Use save and load when you want to persist a fitted PanelMMM and rebuild it later without redefining the whole model configuration in code. Basic round trip The standard workflow is: mmm.fit( X, y, draws=500, tune=500, chains=2, progressbar=False, random_seed=42, ) mmm.save("mmm.nc") loaded = PanelMMM.load("mmm.nc") save() writes the model’s InferenceData to NetCDF. load() reads that file, recreates the PanelMMM configuration from stored metadata, restores loaded.idata, and rebuilds the PyMC graph from the saved training data.