US Health Insurance Bayesian Data Analytics (Group Project)

At least fitting two different models for comparison (based on number of features used, priors, and/or thinning intervals)

Contents:

1. Introduction (of the data)

2. Models (formulation of the models, likelihood, prior)

3. Computation (number burn-in samples, number of post-burn-in samples, number of chains)

4. Model Comparisons (models evaluation and diagnostics

5. Results (choose the "best" model based on (4), describe this model and interpret the results)