mcbayes
Hierarchical Bayesian analysis of R&D expenditure effects on GDP growth in Europe.
Approach
Statistical Rethinking (McElreath) workflow:
- DAG-based causal identification with do-calculus
- Hierarchical Bayesian regression
- NUTS sampler via PyMC
- ArviZ for diagnostics
- Interactive Plotly visualizations
Data
Eurostat API (2016-2022): - GDP by country - R&D expenditure
Why Bayesian
- Quantified uncertainty (not just point estimates)
- Hierarchical structure respects country-level variation
- Prior knowledge incorporated explicitly
- Full posterior for decision-making
Outputs
- Marimo reactive notebook for exploration
- Formal policy note with results
- Interactive visualizations
Related
Applied example of Budget Constraints Drive Emergence - constraints and emergence in statistical modeling.