Bayes estimator

Related lectures (32)

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Covers the probabilistic model for linear regression and its applications in nuclear magnetic resonance and X-ray imaging.

Covers Bayesian decision theory, cost functions, classification, and decision rules.

Explores decision theory, risk functions, and inference in statistical analysis.

Explores optimization and estimation in Monte Carlo methods, emphasizing Bayes-optimal groups and estimators.

Explores the Beta distribution, Bayesian inference, and posterior calculation in the Beta-Bernoulli model.

Covers statistical estimation models, ML estimators, learning machines, practical issues, and challenges in estimation.

Introduces a project to build a dynamic model for simulating racing car performance.

Explores implicit generative models, covering topics like method of moments, kernel choice, and robustness of estimators.

Introduces Fisher Information for parameter estimation based on collected data.

Covers multi-arm bandits in reinforcement learning, exploring the trade-off between exploration and exploitation to minimize regret.