An introduction to Jeffreys priors - 1

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  • Опубліковано 25 лис 2024

КОМЕНТАРІ • 7

  • @spyridon6044
    @spyridon6044 11 місяців тому

    Excellent videos of yours!!! :)

  • @nhlanhlawandile6600
    @nhlanhlawandile6600 5 років тому +3

    On what distributions can we use the Jeffrreys prior?

  • @conradsnowman
    @conradsnowman Рік тому

    Ok so, it's just a change of variables as you would do in an integral?

  • @rols3376
    @rols3376 3 роки тому +2

    so after reading that waffle, I still don't know what a Jefferys prior is.

    • @rangjungyeshe
      @rangjungyeshe Рік тому +4

      It's a way of ensuring that the conclusion you reach from updating your original beliefs (summarised by the prior distrib) with your newly-acquired data (summarised by the likelihood) remain the same, regardless of whether the parameter you're interested in is theta (say, a probability of a certain outcome) or a monotonic function of theta (say, the odds, rather than the probability).
      If this wasn't the case, you'd have two people reaching different conclusions from the same data, simply because they've chosen "somewhat different" (aka monotonically related) ways to capture what they're interested in - which would be a bit weird.
      Indeed, R A FIsher, one of the giants of 20th century statistics, regarded this potential problem in Bayes to be fatal, and he went on to develop "frequentist" methods, which simply duck the problem - and cause all sorts of far worse problems instead!

    • @limzijian98
      @limzijian98 8 місяців тому

      @@rangjungyeshe so basically, its something similar to irregardless of what kind of units you are using , you will still get the same answer ?