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Conjugate Priors and Bayes Point Estimates
Learn how conjugate priors streamline Bayesian inference and discover ways to summarize posterior distributions using Bayes point estimates.
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Doubling Trick - A Clever Strategy to Handle Unknown Horizons
Discover how the Doubling Trick enables online algorithms to adapt to unknown horizons, maintaining competitive regret bounds.
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Exponential Weighted Average Algorithm
Delve into the Exponential Weighted Average Algorithm, its regret bounds, and the mathematical proof ensuring efficient loss minimization.
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Bayesian Machine Learning - Mathematical Foundations
A beginner-friendly guide to Bayesian statistics, explaining priors, likelihoods, posteriors, and real-world examples like coin-flipping to build a clear and intuitive understanding.
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Understanding the Weighted Majority Algorithm in Online Learning
Explore how the Weighted Majority Algorithm achieves robust bounds for adversarial settings by adapting expert weights with every mistake.