pymc-modeling

# Bayesian Modeling Guidance for [COMPANY]

## Model Structure Recommendation
Based on your hierarchical data, I recommend the following model structure:

```python
with pm.Model() as model:
    # Priors for unknown model parameters
    mu = pm.Normal('mu', mu=0, sigma=1)
    sigma = pm.HalfNormal('sigma', sigma=1)
    
    # Hierarchical model
    y_like = pm.Normal('y_like', mu=mu, sigma=sigma, observed=[DATA])
```

## Key Considerations
- **Prior Sensitivity**: Given your industry context, consider testing different prior distributions for `mu`
- **Computational Efficiency**: With your dataset size, I recommend using `pm.sample()` with `chains=4` and `cores=4` for efficient sampling
- **Model Validation**: Implement posterior predictive checks to validate your model

## Next Steps
1. Implement the model structure above
2. Run initial sampling to assess convergence
3. Validate the model using posterior predictive checks
4. Refine the model based on validation results