In this course , the probabilistic programming for statistical inference , STAN, within Bayesian framework has been taught with many examples and mini-project styles .

During my graduate studies in applied mathematics , I did not have the resources which teach me how to write the code and how to tune it , it took me such a long journey to teach myself , this then motivated me to create these tutorials for those who want to explore the richness of the Bayesian inference .

This course , in details , explore the following models in STAN :

- Multi_variate Regression Models

- Convergence and Model Tuning

- Logistic Regression Analysis

- Quadratic Predictive Models

- Hierarchical Models

Ihope this tutorial helps you to think more Bayesian and act more Bayesian.