Informative Priors for MMM

Calculate and apply informative priors for PyMC-Marketing MMM models.

Updated Mar 19, 2026

Search Tags:#marketing #modeling #bayesian #pymc #priors #informative-priors #mmm

Ready to run

npx skills add https://github.com/benmaier/decision-agent-placeholder --skill informative-priors-for-mmm

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: Informative Priors for MMM
Source: https://github.com/benmaier/decision-agent-placeholder/tree/main/decision-packs/mmm/opencode/skills/informative-priors
Command: npx skills add https://github.com/benmaier/decision-agent-placeholder --skill informative-priors-for-mmm

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This expert guide helps modelers improve posterior calibration in Marketing Mix Modeling by instructing how to calculate and apply informative priors based on data characteristics and domain knowledge.

Core Features & Use Cases

Guides setting priors for intercept, channel effects, and adstock parameters using data-driven reasoning.
Demonstrates strategies to tighten or loosen priors to achieve well-calibrated posteriors.
Shows integration with PyMC-Marketing MMM workflows and the model_config structure.

Quick Start

Provide an example of setting informative priors for intercept and channel effects using your MMM dataset.

Frequently Asked Questions about Informative Priors for MMM

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I set informative priors for channel effects in PyMC-Marketing MMM?▼

To set informative priors for channel effects in PyMC-Marketing MMM, you calculate them from scaled data statistics and domain knowledge, then apply these values within the model_config structure to guide posterior calibration.

Why do my MMM posteriors have wide credible intervals even with sufficient data?▼

Wide MMM posteriors often result from poorly calibrated or overly loose priors. You can tighten posterior calibration by applying informative priors derived from data characteristics and domain knowledge to intercept and adstock parameters.

Can I derive priors for adstock parameters from scaled time-series data statistics?▼

Yes, you can derive adstock priors from scaled time-series data statistics. This approach uses data-driven reasoning to configure appropriate distributions across varying channel scales within your MMM dataset.

Does this approach to informative priors work with different prior distributions?▼

Yes, the approach supports handling different prior distributions. It guides configuring intercept, channel effects, and adstock priors by deriving values from data statistics and integrating them into the model_config structure.

What is the best way to calibrate intercept priors for Marketing Mix Models?▼

The best way to calibrate intercept priors for Marketing Mix Models is to calculate them based on data characteristics and domain knowledge, applying the resulting informative priors to achieve well-calibrated posteriors.