📘 Notes: https://robosathi.com/docs/maths/probability/parametric-model-estimation/
This video explains how these components work together under Bayes’ Theorem to form the foundation of Bayesian Statistics — a framework that captures uncertainty, adapts with data, and leads to more reliable predictions.
Learning objectives
✅ Understand how Bayesian Statistics updates beliefs using Bayes’ Theorem
✅ Learn the role of Prior, Likelihood, Posterior, and Evidence
✅ See how data refines our initial assumptions into final conclusions
✅ Explore probability distributions instead of single estimates
✅ Discover how Bayesian methods quantify uncertainty
🎥 Related Videos
✅ https://youtu.be/CWnh1E8F-XU
✅ https://youtu.be/74tLuSlmd-c
🎥 Full Course Link -
✅ https://www.youtube.com/watch?v=Dsz7WcGcnxc&list=FLQqvaMq6Wu40s3RH5ss3P5A&pp=gAQB
🕘 Time Stamp 🕔
00:00:00 - 00:01:56 Bayesian Statistics
00:01:57 - 00:06:22 Revise Bayes Theorem
00:06:23 - 00:08:13 Prior, Likelihood, Posterior Formula
00:08:14 - 00:09:10 Explained with Example
00:09:11 - 00:12:01 Prior Example
00:12:02 - 00:12:36 Likelihood Example
00:12:37 - 00:21:53 Posterior Example
00:21:54 - 00:26:38 Beta Function
00:26:38 - 00:31:27 'Θ' is closer to 1. How?
00:31:28 - Explained with Example
📘 Part of the Math for AI & ML series by RoboSathi
#ai #ml #bayesianstatistics #bayestheorem #probability #statistics #machineLearning #robosathi
