Jabarkan Bayes' theorem dari Normalizing constant dalam bahasa sunda

Bayes' theorem says that the posterior probability méasure is proportional to the product of the prior probability méasure and the likelihood function .  Proportional to implies that one must multiply or divide by a normalizing constant in order to assign méasure 1 to the whole space, i.e., to get a probability méasure.  In a simple discrete case we have

where P(H0) is the prior probability that the hypothesis is true; P(D|H0) is the conditional probability of the data given that the hypothesis is true, but given that the data are known it is the likelihood of the hypothesis (or its paraméters) given the data; P(H0|D) is the posterior probability that the hypothesis is true given the data. P(D) should be the probability of producing the data, but on its own is difficult to calculate, so an alternative way to describe this relationship is as one of proportionality: