MLE for Gaussian: EMV in Gaussian Model

Related lectures (31)

Page 2 of 4

Discusses estimation methods in probability and statistics, focusing on maximum likelihood estimation and confidence intervals.

Delves into maximum likelihood estimators, their properties, and asymptotic behavior, emphasizing consistency and asymptotic normality.

Introduces Fisher Information for parameter estimation based on collected data.

Explores mixture models, including discrete and continuous mixtures, and their application in capturing taste heterogeneity in populations.

Covers the fundamentals of Bayesian estimation, focusing on the application of Bayes' Theorem in scalar estimation.

Explains confidence intervals, parameter estimation methods, and the central limit theorem in statistical inference.

Explores Maximum Likelihood Estimation properties, consistency, and applications in statistical inference.

Covers bias, variance, mean squared error, consistency, and maximum likelihood estimation in the Poisson model.

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

Explores maximum likelihood estimation for density and Bernoulli model, including test reliability and disease screening.