Likelihood principle

Formal sciences
Statistics
Statistical inference
Statistical hypothesis testing

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Frame Synchronization: Systematic Derivation with Likelihood Test

Covers the systematic derivation of a synchronization algorithm with a generalized likelihood test in wireless receivers.

Extreme Value Theory: Statistical Applications

Explores statistical applications of threshold exceedances and GPD modeling in extreme value theory.

Densities and Bayesian Inference: Decision Rules and Bayes Law

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

Gaussian Mixture Models: Likelihood and Covariance Matrix

Explores statistical independence, Gaussian Mixture Models, and fitting data with Gaussian functions.

Maximum Likelihood Estimation: Econometrics

Introduces Maximum Likelihood Estimation in econometrics, covering principles, properties, applications, and specification tests.

Poisson Process Approach

Explores the Poisson process approach in extreme value analysis, emphasizing component-wise transformations and likelihood functions for extreme events.

End of the M-step derivation

Covers the derivation of the M-step for mu_k and Sigma_k, focusing on key quantities and matrices.

Jacamar Data Analysis

Covers jacamar data analysis, smoking data models, and challenges with log-linear models in visual impairment data.

Statistical Inference

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

Fitting data with one Gauss function

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