Lectures related to Relative likelihood

Likelihood Ratio Test: Detection & Estimation

Covers the likelihood ratio test for detection and estimation in statistical analysis.

Discusses the likelihood of spike trains based on generative models and log-likelihood calculations from observed data.

Likelihood Ratio Test: Hypothesis Testing

Covers the Likelihood Ratio Test and hypothesis testing methods using Maximum Likelihood Estimators.

Maximum Likelihood Estimation: Multivariate Statistics

Explores maximum likelihood estimation and multivariate hypothesis testing, including challenges and strategies for testing multiple hypotheses.

Likelihood Ratio Test

Covers the Likelihood Ratio Test in choice models, comparing unrestricted and restricted models through benchmarking and testing different model specifications.

Binary Covariate Impact: 2x2 Contingency Tables

Explores the impact of a binary covariate on binary responses using 2x2 tables.

Latent Variable Models

Explores latent variable models, EM algorithm, and Jensen's inequality in statistical modeling.

Testing: Likelihood Ratio Test

Covers the likelihood ratio test in choice models, benchmarking, and tests for taste variations and nonlinear specifications.

Spin Glasses and Bayesian Estimation

Covers the concepts of spin glasses and Bayesian estimation, focusing on observing and inferring information from a system closely.

Maximum Likelihood: Inference and Model Comparison

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

Model Selection Criteria: AIC, BIC, Cp

Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.

Classification Detection

Covers binary hypothesis testing and decision functions in specific scenarios.

Extreme Value Theory: Statistical Applications

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

Belief Propagation in Stochastic Block Models

Covers the application of Belief Propagation in Stochastic Block Models, focusing on simplifying the process and solving it step by step.

Densities and Bayesian Inference: Decision Rules and Bayes Law

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

Frame Synchronization: Systematic Derivation with Likelihood Test

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

Exponential Family: Properties and Estimation

Explores exponential families, Bernoulli distributions, parameter estimation, and maximum entropy distributions in statistical modeling.

Statistical Inference

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

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.

Likelihood Ratio Tests: Optimality and Applications

Explores the theory and applications of likelihood ratio tests in statistical hypothesis testing.