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
Url: View Details
What you will learn
- Probabilistic Programming with STAN
- Bayesian Inference
- STAN
Rating: 3.4
Level: All Levels
Duration: 8 hours
Instructor: Omid Rezania
Courses By: 0-9 A B C D E F G H I J K L M N O P Q R S T U V W X Y Z
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