Probabilistic Programming with STAN

TDM Probabilistic Programming with STAN: Master the Art of Bayesian Inference! 🧠✨

Course Description: Dive into the world of Probabilistic Programming with STAN, a powerful tool for statistical inference within a Bayesian framework. This comprehensive course is designed to equip you with the knowledge and hands-on experience needed to effectively apply Bayesian methods using STAN. With a blend of theoretical concepts and practical applications, this course will guide you through a series of examples and a capstone mini-project, ensuring you gain both depth and breadth in your understanding of STAN.

Why This Course? 🤔💻

• Real-World Applications: Learn through engaging examples that mirror real-world data analysis scenarios.
• Self-Taught Journey Shared: Omid Rezani, your instructor, shares his journey of self-teaching and the challenges he overcame, providing you with invaluable insights and tips.
• Expertly Structured Content: Each topic is meticulously crafted to facilitate learning and application of concepts.
• Interactive Learning: Engage with interactive content that allows you to apply what you learn in real-time.

Course Highlights:

• Foundational Knowledge: Understand the principles of probabilistic programming and Bayesian inference.
• Practical Tutorials: Gain hands-on experience with Multi-variate Regression Models, Convergence and Model Tuning, Logistic Regression Analysis, Quadratic Predictive Models, and Hierarchical Models.
• Bayesian Mindset: Learn to approach problems with a Bayesian perspective, enhancing your analytical capabilities.

What You'll Learn:

• 📈 Multi-variate Regression Models: Explore the relationship between multiple variables and outcomes.
• ✅ Convergence and Model Tuning: Master techniques to ensure your models converge accurately and perform optimally.
• 🚫 Logistic Regression Analysis: Understand binary outcomes and how to model them with logistic regression.
• 📊 Quadratic Predictive Models: Dive into modeling with quadratic forms for predictions and understanding complex relationships.
• 🔄 Hierarchical Models: Learn about models that capture dependencies between multiple sets of parameters.

Instructor's Insight: "My journey through applied mathematics was marked by the scarcity of resources for learning STAN coding and model tuning. This inspired me to create these tutorials, which I hope will shorten your learning curve and provide you with a clear path to mastering Bayesian inference with STAN." – Omid Rezani

Join Us on a Journey to Mastery! 🚀🎓 Embark on this transformative learning experience and become proficient in Probabilistic Programming with STAN. Whether you're a data scientist, statistician, researcher, or simply someone interested in the power of Bayesian methods, this course will provide you with the skills to extract meaningful insights from your data. Enroll now and take the first step towards becoming an expert in Bayesian statistics! 🌟