Probabilistic Programming with STAN

Increase iterations and reduce the step size to achieve convergence in the Stan model with a quadratic parameter. Use trace plots and R-hat diagnostics to verify convergence.

Explore how to define and use generated quantities in Stan to produce replicated data from model fits, assess convergence with R-hat, and diagnose performance across chains.

Explore convergence diagnosis of Stan models using trace plots across multiple chains, warm-up considerations, and key parameters like alpha and sigma to assess mixing and convergence.

Compare bayesian and non-bayesian fits of a linear model in Stan, examine alpha, beta, and sigma, and plot weight versus height to visualize posterior uncertainty.

Visualize uncertainty in a Bayesian model by plotting posterior draws of alpha and beta for height versus weight, illustrating the credible interval and model uncertainty.

Learn how to execute the Stan model on a diabetes data example, define data and parameters, run iterations with warmups, and assess convergence across four chains via posterior sampling.

Run a hierarchical multi-level model in Stan, using the eight schools example, defining data and hyperparameters, adjusting iterations for convergence, and inspecting results.