Bayes' Theorem Explained: Flipping Conditional Probability

Forward odds you know. Backward odds you need. Bayes' theorem is the formula that flips them.

This 90-second reel derives Bayes from the joint probability identity, plugs in a fair-die example to show P(6 | even) = 1/3, and explains why the same inversion runs every spam filter, every diagnostic test, and every Bayesian model in machine learning.

Posterior = likelihood × prior ÷ evidence — belief, updated by data.

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Видео Bayes' Theorem Explained: Flipping Conditional Probability канала Puthiya Era