Bayes' Theorem

Bayes' Theorem is a mathematical formula used in probability theory and statistics to calculate the conditional probability of an event based on prior knowledge of related events. It is named after Reverend Thomas Bayes, an 18th-century British statistician and theologian. The theorem provides a way to update the probability of a hypothesis as new evidence becomes available. In data science and artificial intelligence, Bayes' Theorem is used in Bayesian inference, a statistical technique for updating probabilities based on new data. It is also used in machine learning algorithms such as Naive Bayes, which is a probabilistic classifier that uses Bayes' Theorem to predict the probability of a given class based on the input features. Bayes' Theorem is a fundamental concept in Bayesian statistics and has applications in various fields including natural language processing, computer vision, and recommendation systems.
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