Markov Chains Explained Visually

This video provides an introduction to Markov chains, explaining their underlying principles, including states, transitions, probabilities, and the memoryless property. It further explores the concept of stationary distributions, and illustrates the application of Markov chains in Google's PageRank algorithm, highlighting their broader utility in modeling systems that evolve randomly over time. Socials: ====== Patreon:   / thesyntheticmind   BuyMeACoffee: https://buymeacoffee.com/thesynthetic... TikTok:   / the.synthetic.mind   Chapters: ======== 0:00 Intro to Markov Chains 1:39 States, Transitions, and Probabilities 3:47 Weather Model Example 5:46 Stationary Distributions 8:00 PageRank Application 10:24 Applications & Key Concepts