Bayesian Theory and Graphical Models

Dive into a comprehensive 2.5-hour lecture series on Bayesian Theory and Graphical Models, exploring key concepts across multiple sections. Begin with an in-depth look at foundational principles, then progress through advanced topics including probability theory, statistical inference, and graphical model applications. Examine the intricacies of Bayesian networks, learn about parameter estimation techniques, and understand the practical implementation of these powerful statistical tools. Gain valuable insights into decision-making under uncertainty and discover how to apply Bayesian methods to real-world problems in various fields such as machine learning, data analysis, and artificial intelligence.

Syllabus

Bayesian Theory and Graphical Models - Sec. 1.1 (27 min).
Bayesian Theory and Graphical Models - Sec. 1.2-1.4 (21 min).
Bayesian Theory and Graphical Models - Sec. 1.5-1.6 (9 min).
Bayesian Theory and Graphical Models - Sec. 2.1-2.4 (23 min).
Bayesian Theory and Graphical Models - Sec. 4 (14 min).
Bayesian Theory and Graphical Models - Sec. 5 (33 min).
Bayesian Theory and Graphical Models - Sec. 6 (9 min).