Stein's Paradox: Shrinking to Improve All Estimates

We explore the counterintuitive James–Stein estimator: why pooling multiple normal means and shrinking toward a common center lowers total risk in three or more dimensions. We'll unpack geometric intuition, the Brownian motion connection, and the practical implications for statistics and AI models.

Note: This podcast was AI-generated, and sometimes AI can make mistakes. Please double-check any critical information.

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