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You have a jar with five green, three blue marbles. First random marble you pick is green, what is the chance you get blue next? There is no use for bayes here.

You have a jar with five green, three blue marbles. First random marble you pick is green, what is the chance you get blue next? There is no use for bayes here. Now you have two jars you can't see inside of, 5g/3b and 3b/5r. You take one jar and want to guess which one you picked. You start pulling marbles and updating your priors until you reach an acceptable certainty. Now you have to use bayes or similar. These are tools, not ideologies. People who pit these tools against each other are demagogues.

NeutralCrane 1y ago
> You have a jar with five green, three blue marbles. First random marble you pick is green, what is the chance you get blue next? There is no use for bayes here. There’s no use for Frequentism there either, it’s basic probability theory.
- nurettin 1y ago
  And "frequentism" uses that basic probability theory, unless you have a "frequentist" answer to the first example that is something other than 3/7?
  - ratorx 1y ago
    Are you implying that Bayesian doesn’t use basic probability theory or gives a different answer? To calculate the simplest possible conditional probability, you need to have defined probability first.
    - nurettin 1y ago
      The first example is not a demonstration for bayesian inference. It uses a simple tool used in the "frequentist" approach. The bayesian approach example is below that. My point is the two are mutually exclusive in these scenarios. I am trying to demonstrate that there are formulas for different scenarios and you don't pit them against each other. The response to that was just pedantic.
      - ratorx 1y ago
        Your example is flawed because the example has nothing to do with the difference between Bayesian and Frequentist inference, because neither approach is needed. In the same way that you don’t use Bayesian approach, you also have not done anything frequentist. All you need for this problem is to calculate some probabilities. There is nothing “frequentist” about calculating probabilities (and nothing Bayesian about it either), because it is more fundamental than that. There is nothing Bayesian or Frequentist about basic probability theory. They are both interpretations that rely on the existence of probability theory to make sense. So solving a problem with basic probability theory and claiming I did “Frequentism” is correct, but meaningless.
        nurettin 1y ago
        Maybe you would care to write a sensible example, then?
        ratorx 1y ago
        In my opinion, Bayes is the more general approach. But the Frequentist approach can be simpler if your prior is uniform and you only care about the output, not the distribution. E.g. Neural Networks, where the computational cost makes modelling the problem in a Bayesian way much more expensive. Or you can say the Frequentist approach is simpler for estimating the probability of heads for a biased coin, as long as you have no prior and only care about the most likely value, not the distribution.
        nurettin 1y ago
        It is not about "more expensive computation" that is just absolute nonsensical rhetoric. Again, given these basic examples, probability of outcomes have nothing to do with bayes. Bayes needs at least two scenarios to distinguish between. Simply P and 1-P reduces the formula into a counting problem. I am done with this.
        1y ago

NeutralCrane 1y ago

> You have a jar with five green, three blue marbles. First random marble you pick is green, what is the chance you get blue next? There is no use for bayes here. There’s no use for Frequentism there either, it’s basic probability theory.

nurettin 1y ago
And "frequentism" uses that basic probability theory, unless you have a "frequentist" answer to the first example that is something other than 3/7?
- ratorx 1y ago
  Are you implying that Bayesian doesn’t use basic probability theory or gives a different answer? To calculate the simplest possible conditional probability, you need to have defined probability first.
  - nurettin 1y ago
    The first example is not a demonstration for bayesian inference. It uses a simple tool used in the "frequentist" approach. The bayesian approach example is below that. My point is the two are mutually exclusive in these scenarios. I am trying to demonstrate that there are formulas for different scenarios and you don't pit them against each other. The response to that was just pedantic.
    - ratorx 1y ago
      Your example is flawed because the example has nothing to do with the difference between Bayesian and Frequentist inference, because neither approach is needed. In the same way that you don’t use Bayesian approach, you also have not done anything frequentist. All you need for this problem is to calculate some probabilities. There is nothing “frequentist” about calculating probabilities (and nothing Bayesian about it either), because it is more fundamental than that. There is nothing Bayesian or Frequentist about basic probability theory. They are both interpretations that rely on the existence of probability theory to make sense. So solving a problem with basic probability theory and claiming I did “Frequentism” is correct, but meaningless.
      - nurettin 1y ago
        Maybe you would care to write a sensible example, then?
        ratorx 1y ago
        In my opinion, Bayes is the more general approach. But the Frequentist approach can be simpler if your prior is uniform and you only care about the output, not the distribution. E.g. Neural Networks, where the computational cost makes modelling the problem in a Bayesian way much more expensive. Or you can say the Frequentist approach is simpler for estimating the probability of heads for a biased coin, as long as you have no prior and only care about the most likely value, not the distribution.
        nurettin 1y ago
        It is not about "more expensive computation" that is just absolute nonsensical rhetoric. Again, given these basic examples, probability of outcomes have nothing to do with bayes. Bayes needs at least two scenarios to distinguish between. Simply P and 1-P reduces the formula into a counting problem. I am done with this.
        1y ago