Relating to statistical methods based on Bayes' theorem, which describes how to update probability estimates as new evidence becomes available. Fundamental to modern data analysis and machine learning.
Named after Reverend Thomas Bayes (1701-1761), an English Presbyterian minister and mathematician. His theorem on conditional probability was published posthumously and became foundational to statistical inference and modern artificial intelligence.
Bayesian thinking revolutionizes how we approach uncertainty by treating probability as a degree of belief that can be updated with new evidence. This approach is so powerful that it underlies everything from spam filters to medical diagnosis - essentially teaching machines to think more like humans do naturally.
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