Markov Chains MADE EASY | Linear Algebra APPLICATIONS

In this video, we cover linear algebra applications. We show how eigenvalues and eigenvector can be used to determine steady states of Markov chains. We discuss probability vectors and transition matrices, which are stochastic matrices. We prove that every stochastic matrix has 1 as its eigenvalue. You may also be interested in my full linear algebra course: https://www.youtube.com/playlist?list=PLxQVx0jlffqfMhwn-i9q161gGxURlk7wb Students often ask is linear algebra important and is linear algebra hard. They wonder can anyone learn linear algebra and why some can't understand linear algebra. It is important to show to the students how linear algebra is used in data science, machine learning, AI, computer science, image processing, computer graphics, and in real life in general. This is a free video course in linear algebra for everyone, even beginners. It covers linear algebra with applications. CHAPTERS: 0:00 A simple example of a Markov chain: changing weather 3:11 Transition matrix of a Markov chain 5:00 Probability vectors and stochastic matrices 6:00 Eigenvalues of stochastic matrices 7:37 Steady-state vectors 8:26 How to find a steady-state vector 9:54 Long-term behavior of a Markov chain 10:25 Steady states might not be reachable 11:01 Regular transition matrices and steady states 12:10 Your homework assignment #mathflipped