Posterior Probability

In Bayesian statistics, posterior probability is the probability of a hypothesis or model given the observed data. It is calculated using Bayes' theorem, which involves multiplying the prior probability of the hypothesis by the likelihood of the data given the hypothesis, and then normalizing by the probability of the data. The posterior probability is an updated probability that takes into account the observed data and can be used to make predictions or decisions. It is a key concept in Bayesian inference and is used in many applications, including machine learning, natural language processing, and image recognition.
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