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Prior, Posterior and Likelihood in Bayers Theorem in layman terms
Bayes' theorem is a method for updating probabilities by combining prior beliefs with new evidence to form more accurate conclusions. The article explains this through everyday examples and a detailed
medical testing scenario, demonstrating how initial probabilities can be significantly different from final probabilities after considering all factors.

Reasons to Read -- Learn:

  • how to better understand probability in real-world situations, using practical examples like weather prediction and medical testing that demonstrate why our intuitive understanding of probability often leads us astray.
  • how to break down complex probabilistic reasoning into three manageable components (prior, likelihood, and posterior), making it easier to analyze and solve probability problems in any context.
  • why a 99% accurate medical test can still result in only a 9% chance of actually having a disease, understanding how base rates and false positives dramatically impact probability calculations.
  • 3 min readauthor: Pankaj Agrawal
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