Past Present Future The History of Revolutionary Ideas: The Bayesian Revolution w/David Spiegelhalter
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Mar 16, 2025 David Spiegelhalter, a leading statistician famed for simplifying complex statistical ideas, delves into the intriguing evolution of Bayesian probability, tracing its roots from Thomas Bayes in the 18th century to its modern-day relevance. He unpacks how this unconventional approach fundamentally shifts our understanding from prediction to causation. The discussion covers its historical implications, applications in AI and political polling, as well as the ongoing controversies surrounding Bayesian methods. Spiegelhalter highlights the importance of humility and acknowledging personal biases in statistical interpretation.
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Bayes' Inverse Probability
- Thomas Bayes solved the "inverse problem" of probability, inferring underlying chances from observed events.
- This differed from the conventional focus on predicting future events based on known probabilities.
Subjective Probability
- Bayes defined probability as a degree of belief, linked to expected gain in a gamble.
- This subjective view differed from definitions based on long-run frequencies or symmetries.
Bayes' Billiard Table
- Bayes illustrated his theory with a thought experiment involving balls thrown onto a table.
- The experiment aimed to infer the position of a hidden line based on which side subsequent balls landed.
