TL;DR
A statistical framework that updates beliefs via Bayes' theorem: \( p(\theta|data) \propto p(data|\theta) p(\theta) \). The posterior combines prior knowledge with observed data through the likelihood.
Bayesian Inference
A statistical framework that updates beliefs via Bayes' theorem: \( p(\theta|data) \propto p(data|\theta) p(\theta) \). The posterior combines prior knowledge with observed data through the likelihood.
Why it matters for interviews
Bayesian methods are increasingly used in portfolio optimization (Black-Litterman), signal combination, and regime detection. They naturally incorporate uncertainty and prior knowledge, avoiding overfitting issues common in frequentist approaches.
Definition and Mathematical Foundation
A statistical framework that updates beliefs via Bayes' theorem: \( p(\theta|data) \propto p(data|\theta) p(\theta) \). The posterior combines prior knowledge with observed data through the likelihood.
Application in Quantitative Finance
Bayesian methods are increasingly used in portfolio optimization (Black-Litterman), signal combination, and regime detection. They naturally incorporate uncertainty and prior knowledge, avoiding overfitting issues common in frequentist approaches.
Related Concepts
Related Terms
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