Probabilistic Programming with STAN
Parametric Bayesian Methods
Description
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
I hope this tutorial helps you to think more Bayesian and act more Bayesian.
What You Will Learn!
- Probabilistic Programming with STAN
- Bayesian Inference
- STAN
Who Should Attend!
- Anyone who is interested to know how to start a project applying Bayesian and finish it in Bayesian