Bayesian Probability Theory

Bayesian Probability Theory is a statistical approach that uses Bayes' theorem to update the probability of a hypothesis as more evidence or information becomes available. It involves the use of prior knowledge or beliefs about the hypothesis, which are updated using new data to arrive at a posterior probability distribution. This approach is particularly useful in situations where data is limited or uncertain, and where there is a need to make decisions based on incomplete information. Bayesian Probability Theory has applications in a wide range of fields, including machine learning, artificial intelligence, and data science.
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