(ML 18.2) Ergodic theorem for Markov chains
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- Опубліковано 28 вер 2024
- 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.
A great series with much needed emphasis on the intuition and mathematics.
Best lecture series out there that I have encountered yet.
Thanks
The best ML lectures out there
These are so great! Masterful presentation.
Thank you very much!
this is really helpful thank you :)
well done , from a yogi to a monk...
Great Presentations!!! Would you have any book recommendations for Markov Chain Monte Carlo!?
what does drawn according to a Markov Chain really mean?? someone plls explain!
Still one of the best stats courses on the tube
Hi, what is the name of the program used for the presentation?
thanks!
thanks!!
yolda karsilasmisizcasina
what is discrete space at 1:40
'anything that can happen eventually will happen.' I think this expression is the simplest way to describe ergodicity.
Reminds me of Murphy's law lol.
very vivid. Thanks
droll: "Didn't the Police have an album out decades ago called "Ergodicity"?".....LOL
Too much theory, no examples
Very clear and exhaustive explanation! Thank you! Could you provide also some references? Textbooks? Scientific publications? Thanks again!
well done.great. thanks.
Why do you say it converges almost surely if it converges with probability one?
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?
excuse me if we have a sequence of random variables
when we can say they are form a markove chain
Great video