Decision Rules: Maximum a Posteriori Decision

Related lectures (32)

Page 2 of 4

Covers binary classification, hypothesis testing, likelihood ratio tests, and decision rules.

Covers the Bayes discriminant rule for allocating individuals to populations based on measurements and prior probabilities.

Discusses Bayesian inference for the mean of a Gaussian distribution with known variance, covering posterior mean, variance, and MAP estimator.

Explores hypothesis testing using Wilks' Theorem, likelihood ratio statistics, p-values, interval estimation, and confidence regions.

Covers classification concepts, medical screening tests, and decision rules.

Covers iterative weighted least squares, Poisson regression, mixed models, and likelihood ratio statistic.

Explores maximum likelihood inference, model selection, and comparing models using likelihood ratios.

Covers the sexing of guppy fish using Bayesian inference and decision rules.

Explores the Method of Moments, Bias-Variance tradeoff, Consistency, Plug-In Principle, and Likelihood Principle in point estimation.

Explains Gaussian functions, modeling data, likelihood function, and maximum likelihood optimization.