(ML 18.2) Ergodic theorem for Markov chains

Statement of the Ergodic Theorem for (discrete-time) Markov chains. This gives conditions under which the average over time converges to the expected value, and under which the marginal distributions converge to the stationary distribution.

[@aditbhardwaj]

A great series with much needed emphasis on the intuition and mathematics. Best lecture series out there that I have encountered yet. Thanks

[@jacobfeldman3558]

The best ML lectures out there

[@decarbfriendsunikassel1831]

Still one of the best stats courses on the tube

[@micheldvorsky]

These are so great! Masterful presentation.

[@canerates6180]

this is really helpful thank you :)

[@federicaluppino1211]

Very clear and exhaustive explanation! Thank you! Could you provide also some references? Textbooks? Scientific publications? Thanks again!

[@valentinagiaconi4508]

Thank you very much!

[@jacoboribilik3253]

Great video

[@nautiyogi8386]

well done , from a yogi to a monk...

[@fosheimdet]

Why do you say it converges almost surely if it converges with probability one?

[@FrankieRussoVideo]

Great Presentations!!! Would you have any book recommendations for Markov Chain Monte Carlo!?

[@mikesmith7249]

Would it be accurate to say that in an ergodic system, every point is defined between two ranges, one on an x axis and the other on the y?

[@2011sjw]

well done.great. thanks.

[@ashalyngdoh7622]

what does drawn according to a Markov Chain really mean?? someone plls explain!

[@asmaawasfy3150]

excuse me if we have a sequence of random variables when we can say they are form a markove chain

[@liftmyskill]

what is discrete space at 1:40

[@eladlavie1]

Hi, what is the name of the program used for the presentation? thanks!

[@mcab2222]

thanks!!

[@caglabalcik6865]

yolda karsilasmisizcasina

[@yifansong547]

very vivid. Thanks

[@klam77]

droll: "Didn't the Police have an album out decades ago called "Ergodicity"?".....LOL

[@하민박사]

'anything that can happen eventually will happen.' I think this expression is the simplest way to describe ergodicity.

[@fatihsaidduran]

Reminds me of Murphy's law lol.

[@nitinkumarmittal4369]

Too much theory, no examples