Tag: marketing-mix-modeling
All the articles with the tag "marketing-mix-modeling".
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Collinearity Doesn't Break Your Model — It Tells You What Your Data Can't Separate
A high VIF isn't a technical failure to fix — it's a signal that your data can't distinguish two effects. The right response is better data or informative priors, not variable deletion.
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Simulation-Based Calibration: The Missing Test in Your Bayesian Workflow
Passing R-hat and ESS tells you the sampler converged — it says nothing about whether the inference is correct. Simulation-Based Calibration is the test that checks the inference itself.
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Read the Diagnostics First
R-hat, ESS, and divergences are not bureaucratic hurdles before you report a ROAS — they're the sampler telling you whether its output is valid. Here's what each one actually measures and what it reveals when it fails.
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mmm-framework 0.1.0 Is on PyPI
The first public release of mmm-framework — a Bayesian marketing-mix modeling library built on PyMC-Marketing, designed around methodological rigor instead of specification shopping. pip install mmm-framework.