Probabilistic Programming with STAN

Probabilistic Programming with STAN

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. 

Parametric Bayesian Methods

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What you will learn

• Probabilistic Programming with STAN
• Bayesian Inference
• STAN

Rating: 3.4

Level: All Levels

Duration: 8 hours

Instructor: Omid Rezania