(ML 18.2) Ergodic theorem for Markov chains

Поділитися
Вставка
  • 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.

КОМЕНТАРІ • 24

  • @aditbhardwaj
    @aditbhardwaj 8 років тому +25

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

  • @jacobfeldman3558
    @jacobfeldman3558 8 років тому +10

    The best ML lectures out there

  • @micheldvorsky
    @micheldvorsky 13 років тому +5

    These are so great! Masterful presentation.

  • @valentinagiaconi4508
    @valentinagiaconi4508 7 років тому +3

    Thank you very much!

  • @canerates6180
    @canerates6180 7 років тому +3

    this is really helpful thank you :)

  • @nautiyogi8386
    @nautiyogi8386 6 років тому +1

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

  • @FrankieRussoVideo
    @FrankieRussoVideo 5 років тому

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

  • @ashalyngdoh7622
    @ashalyngdoh7622 4 роки тому

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

  • @decarbfriendsunikassel1831
    @decarbfriendsunikassel1831 2 роки тому +2

    Still one of the best stats courses on the tube

  • @eladlavie1
    @eladlavie1 7 років тому

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

  • @muratcan__22
    @muratcan__22 5 років тому +1

    thanks!!

  • @liftmyskill
    @liftmyskill 6 років тому

    what is discrete space at 1:40

  • @하민박사
    @하민박사 3 роки тому

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

  • @yifansong547
    @yifansong547 5 років тому

    very vivid. Thanks

  • @klam77
    @klam77 5 років тому

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

  • @nitinkumarmittal4369
    @nitinkumarmittal4369 4 роки тому

    Too much theory, no examples

  • @federicaluppino1211
    @federicaluppino1211 5 років тому +1

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

  • @2011sjw
    @2011sjw 11 років тому +2

    well done.great. thanks.

  • @fosheimdet
    @fosheimdet 4 роки тому

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

  • @mikesmith7249
    @mikesmith7249 3 роки тому

    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?

  • @asmaawasfy3150
    @asmaawasfy3150 8 років тому

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

  • @jacoboribilik3253
    @jacoboribilik3253 4 роки тому

    Great video