Probabilistic Numerics: Turning Uncertainty into Insight

In this Deep Dive, we explore probabilistic numerics—the blend of numerical analysis, probability, and machine learning that outputs distributions rather than single numbers. We’ll unpack why uncertainty matters, how Bayesian ideas add built‑in error bars, and how uncertainty can be carried through chains of computations. Real‑world relevance shines in integration, optimization, linear algebra, and differential equations, with examples from climate modeling and finance. We’ll also trace the history (Poincaré and Solin), touch on game‑theoretic perspectives, and point to practical tools like ProbNum.

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

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