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Second channel video: 100k Q&A Google form: “A drunk man will find his way home, but a drunk bird may get lost forever.“ What is this sentence about? In 2D, the random walk is “recurrent“, i.e. you are guaranteed to go back to where you started; but in 3D, the random walk is “transient“, the opposite of “recurrent“. In fact, for the 2D case, that also means that you are guaranteed to go to ALL places in the world (the only constraint is, of course, time). [Think about why.] Markov chains are also an important tool in modelling the real world, and so I feel like this is a good excuse for bringing it up. At the end, I also compare this phenomenon to Stein's paradox - in both cases, there is a cutoff between 2 and 3 dimensions, and they have similar intuitive explanation - is that a coincidence? Video chapters: 00:00 Introduction 00:59 Chapter 1: Markov chains 03:20 Chapter 2: Recurrence and transience 10:08 Chapter 3: Back to random walks Further reading: Larry Brown’s paper: ~lbrown/Papers/1971b Admissible estimators, recurrent diffusions, and insoluble boundary value Using electric circuits to prove recurrence / trasience: ~pw/math100w13/ More complicated, but more general proof: ~morrow/336_19/papers19/ Actual probability for 3D random walk to come back: Other than commenting on the video, you are very welcome to fill in a Google form linked below, which helps me make better videos by catering for your math levels: If you want to know more interesting Mathematics, stay tuned for the next video! SUBSCRIBE and see you in the next video! If you are wondering how I made all these videos, even though it is stylistically similar to 3Blue1Brown, I don't use his animation engine Manim, but I will probably reveal how I did it in a potential subscriber milestone, so do subscribe! Social media: Facebook: Instagram: Twitter: Patreon: (support if you want to and can afford to!) Merch: Ko-fi: [for one-time support] For my contact email, check my About page on a PC. See you next time!
