Informative Priors for MMM
CommunityCalibrate MMM with principled priors.
Authorbenmaier
Version1.0.0
Installs0
System Documentation
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.
Dependency Matrix
Required Modules
None requiredComponents
Standard package💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: Informative Priors for MMM Download link: https://github.com/benmaier/decision-agent-placeholder/archive/main.zip#informative-priors-for-mmm Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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