Maximum Likelihood Decision

Related lectures (30)

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

Covers the derivation of the EM algorithm for the Gaussian Mixture Model.

Explores maximum likelihood inference, comparing models based on likelihood ratios and demonstrating with a coin example.

Covers the basics of probability theory, random variables, joint probability, and inference.

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

Explains the E-step and M-step calculations in the Gaussian Mixture Model, including the pseudocode of the EM algorithm.

Introduces the fundamentals of supervised learning, including loss functions and probability distributions.

Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.

Covers likelihood ratio statistic, confidence intervals, and hypothesis testing concepts.

Introduces Discrete Choice Analysis, covering scale, depth, data collection, and statistical inference.