Bayesian Modelling with Regression ( From A to Z ) with R

🎓 Course Title: Bayesian Modelling with Regression (From A to Z) with R

Course Description

"No thief, however skillful, can rob one of knowledge, and that is why knowledge is the best and safest treasure to acquire." - L. Frank Baum, The Lost Princess of Oz

Welcome to the Bayesian Modelling with Regression course! As I (Omid Rezani) recall my own journey through the labyrinth of Applied Mathematics, I remember being submerged in a sea of Bayesian theory and complex equations during my graduate studies. It wasn't until I embarked on my first project that I truly understood the struggle of applying these methods. The scarcity of resources that guided one from start to finish was palpable.

This course is designed to be your personal guide, your bridge from uncertainty to proficiency in Bayesian regression using R. Whether you're a student, a researcher, or a practitioner looking to harness the power of probabilistic inference, this course will equip you with the knowledge and practical skills necessary to navigate the world of Bayesian modelling with confidence.

Course Highlights:

• Foundational Concepts: We'll begin with a gentle introduction to the key concepts from traditional regression before diving into Bayesian techniques. This will serve as a warm-up and ensure that everyone is on the same page.

• Bayesian vs. Non-Bayesian Frameworks: By running the same models in both frameworks, you'll clearly see the differences between these two approaches and understand how to interpret the results effectively.

• Real Data Application: We'll apply Bayesian methods to real-world data, covering topics like model comparison, selection, cross validation, and uncertainty visualization.

• Hands-On Learning: Each concept will be illustrated with R code, allowing you to practice and deepen your understanding in real time.

What You Will Learn:

• The Basics of Bayesian Modelling: Understanding the principles behind Bayesian methods and how they differ from classical statistics.

• Bayesian Regression Techniques: Mastering Bayesian regression through practical examples, including linear and logistic regression.

• Model Comparison and Selection: Learning how to choose the best model for your data using Bayesian factors and other model comparison tools.

• Cross Validation: Understanding the importance of cross validation in assessing model performance.

• Visualizing Uncertainty: Gaining insights into interpreting and visualizing uncertainty in predictions, a crucial aspect of probabilistic modelling.

Course Structure:

1. Warm-Up: A review of key concepts from non-Bayesian regression to prepare for Bayesian methods.

2. Bayesian Modelling Framework: Introduction to the Bayesian approach and understanding Bayesian inference.

3. Bayesian Regression with R: Step-by-step guidance on implementing Bayesian regression using R.

4. Real Data Analysis: Applying Bayesian methods to real datasets, including case studies and practical examples.

5. Model Evaluation and Comparison: Techniques for evaluating model fit, comparing models, and selecting the best model.

6. Cross Validation and Prediction: Mastering cross validation and making predictions with confidence.

7. Uncertainty Visualization: Learning to visualize the posterior distributions and credible intervals effectively.

Join me on this exciting journey into the world of Bayesian modelling with regression. Together, we'll unlock the treasures of probabilistic inference and enhance our ability to analyze data with greater sophistication and understanding. I look forward to learning with you and building a community of Bayesian enthusiasts dedicated to advancing our knowledge and skills.

Let's embark on this adventure... 🚀

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