Tutorial introducing stochastic processes and Markov chains. Learn how to simulate a simple stochastic process, model a Markov chain simulation and code out the n-step transition matrix for a Markov chain. GitHub repo containing the notebook under "Statistics with Python" GitHub Repo: %20with%20Python/An_Intro_to_Markov_chains_with_Python!.ipynb CONNECT: LinkedIn: GitHub: Twitter: Odysee: @adriandolinay:0 Medium: @adriandolinay |-Video Chapters-| 0:00 - Intro 0:24 - Definition of stochastic process 1:15 - Simulating a stochastic process with gambler's ruin 7:40 - Probability of gambler's ruin 11:26 - Definition of Markov chains 14:17 - Markov transition graph 15:38 - Coding a Markov chain simulation 23:15 - Memorylessness of Markov chains 24:05 - Simulating an n-step transition matrix 29:21 - Stationary distribution of a Markov chain 31:16 - 2-step transition matrix given an initial distribution 33:42 - References and additional learning