3. Bayes Estimation Example

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  • Опубликовано: 22 дек 2024

Комментарии •

  • @jonas25505
    @jonas25505 6 лет назад +4

    All of your Videos are very helpfull to me. Thank you.

  • @afifkhaja
    @afifkhaja 7 лет назад +18

    You beat the hell out of my textbook. Thank you!

    • @ProfessorKnudson
      @ProfessorKnudson  7 лет назад +3

      I'm happy to hear it! Good luck with your studies.

  • @nvl901
    @nvl901 6 лет назад +6

    This was super helpful, thank you!

  • @aminulhaque8026
    @aminulhaque8026 6 лет назад +5

    I thought the mean of the gamma distribution is Alpha/beta, so shouldn't our results be (sum xi + alpha) / (1 ( n + 1/beta)) instead of multiplying them?

    • @ProfessorKnudson
      @ProfessorKnudson  5 лет назад +21

      There's two ways to write the gamma distribution: one uses the "shape" parameter and the other uses the "rate" parameter. Essentially one has exp(-x * beta) and (beta)^alpha while the other has exp(-x/beta) and (1/beta)^alpha. The former has mean alpha/beta while the latter has mean alpha * beta.

  • @linkeris7994
    @linkeris7994 6 лет назад +1

    very excellent, ​your video helps me a lot, thx!

  • @raghiunnikrishnan26
    @raghiunnikrishnan26 6 лет назад +2

    It's very helpful.Thank you😄

  • @ThefamousMrcroissant
    @ThefamousMrcroissant 6 лет назад +9

    I can replicate it, but I've no idea what the hell I'm doing

    • @ProfessorKnudson
      @ProfessorKnudson  5 лет назад +1

      I think if you start doing some Bayesian statistical analysis, this will be better motivated and you'll see why you're doing it.

  • @zehraahmed8879
    @zehraahmed8879 6 лет назад +1

    good stuff. Helped a
    lot!

  • @infinity-and-regards
    @infinity-and-regards 6 лет назад

    Thanks, but why are you sure you can derive the posterior with proportionality? i.e. are you sure the constant term will be the same as if you would calculate it all the way (without dropping the constant terms)?