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In this tutorial, I explain the theoretical and mathematical underpinnings of Markov Chains. While I explain all the fundamentals, the focus is on the properties of Markov chains that can be leveraged to apply the Law of Large numbers when doing Bayesian Inference. I also am sharing the interesting story of what led to the invention of what we now know as Markov Chains. Please use the timestamps below to skip over the historical aspects if you are not interested in that. Chapters: 00:00 Introduction & Recap 01:40 What is meant by independent sampling? 02:54 Historical aspects and event that led to the invention of Markov Chains 09:07 The rest of the tutorial The example used in this tutorial is taken from the lecture notes prepared by Dr. Rachel Fewester. She is a professor of Statistics at the University of Auckland, NZ. You will find the lecture notes here in form of 2 chapters-: https://www.stat.auckland.ac.nz/~fews... https://www.stat.auckland.ac.nz/~fews... Some of the animations in the tutorial were created using manim (the toolkit authored by 3Blue1Brown). I used the community version - https://github.com/manimcommunity/manim/ - and I want to express my gratitude towards all the hard work that 3Blue1Brown and Manim Community has put into this library. #markovchains #montecarlo #bayesianstatistics