Saya mau tau Dina minangkaan, dari Inferensi Bayes. Jelaskan dalam bahasa sunda

lamun p(a) mangrupa sebaran beta nu mibanda paraméter m0 sarta n0,

mangka posterior ogé sebaran beta nu mibanda paraméter m+m0 jeung n+n0.

A conjugate prior is a prior distribution, such as the beta distribution in the above example, which has the property that the posterior is the same type of distribution.

What is "Bayesian" about Proposition 9 is that Bayes presented it as a probability for the paraméter p. That is, not only can one compute probabilities for experimental outcomes, but also for the paraméter which governs them, and the same algebra is used to maké inferences of either kind. Interestingly, Bayes actually states his question in a way that might maké the idéa of assigning a probability distribution to a paraméter palatable to a frequentist. He supposes that a billiard ball is thrown at random onto a billiard table, and that the probabilities p and q are the probabilities that subsequent billiard balls will fall above or below the first ball. By making the binomial paraméter p depend on a random event, he cleverly escapes a philosophical quagmire that he most likely was not even aware was an issue.