Meridian Open-Source
Build, run, and analyze Marketing Mix Models (MMM) with Meridian's advanced Bayesian modeling and causal inference capabilities.
Explore the Meridian Open-Source Library
Basics
Introduction to Meridian, glossary, and FAQs.
User Guide
Step-by-step instructions for installation and using the Meridian library.
Modeling guides
Get guidance on every step of your Meridian journey:
- Pre-modeling: Gather the right data and prepare for modeling.
- Applied Modeling: Set up and run Meridian, including advanced customization and calibration.
- Post-modeling: Evaluate model health, interpret results, and optimize budgets.
- Bayesian Modeling & Causal Inference Theory: Explore the theoretical foundations of Meridian.
Code Examples
Explore end-to-end examples and use cases in interactive Colab notebooks.
API Reference
Detailed documentation for all classes and functions in the Meridian library.
Changelog
Stay up to date with the latest releases, new features, and bug fixes in the Meridian library.
Educational Video Series
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Introduction to Meridian
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Demo of Meridian
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Geo vs National Level Modeling
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Introduction to Priors
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Treatment Prior Types
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Calibrate Treatment Priors
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Knots in Meridian
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Incremental Outcome, ROI, mROI, Response Curves
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Controls, Mediators & Treatments in Meridian
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Adstock and Hill
Recommended Learning Paths
Meridian is designed for cross-functional measurement teams. Depending on your role, we recommend the following paths:
Marketing Analysts & Business Users
Start with Pre-modeling to collect and organize your data. Then explore the Post-modeling guides to interpret visualizations, evaluate ROI, and run budget optimizations.
Data Scientists & Technical Practitioners
Dive into Applied Modeling to configure the Bayesian model and customize priors. Explore Bayesian Modeling & Causal Inference Theory to understand the mathematical and theoretical foundations.