Lectures related to MLE for Gaussian: EMV in Gaussian Model

Estimation and Confidence Intervals

Explores bias, variance, and confidence intervals in parameter estimation using examples and distributions.

Gaussian Mixture Models & Noisy Signals

Explores Gaussian mixture models and denoising noisy signals using a probabilistic approach.

Statistics for Data Science: Introduction to Statistical Methods

Covers the fundamental concepts of statistics and their application in data science.

Maximum Likelihood Estimation

Covers Maximum Likelihood Estimation, focusing on ML Estimation-Distribution, Shrinkage Estimation, and Loss functions.

Estimation Methods

Covers various methods for estimating model parameters, such as method of moments and maximum likelihood estimation.

Statistical Theory: Maximum Likelihood Estimation

Explores the consistency and asymptotic properties of the Maximum Likelihood Estimator, including challenges in proving its consistency and constructing MLE-like estimators.

Basic Principles of Point Estimation

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

Maximum Likelihood Theory & Applications

Covers maximum likelihood theory, applications, and hypothesis testing principles in econometrics.

Fisher Information, Cramér-Rao Inequality, MLE

Explains Fisher information, Cramér-Rao inequality, and MLE properties, including invariance and asymptotics.

Estimation: Measures of Performance

Explores estimation measures of performance, including the Cramér-Rao bound and maximum likelihood estimation.

Estimation Methods in Probability and Statistics

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

Maximum Likelihood Estimation

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

Intro to Quantum Sensing: Parameter Estimation and Fisher Information

Introduces Fisher Information for parameter estimation based on collected data.

Mixture Models: Simulation-based Estimation

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

Bayesian Estimation

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

Confidence Intervals: Definition and Estimation

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

Maximum Likelihood Estimation: Properties and Consistency

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

Bias, Variance, Consistency, EMV

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

Maximum Likelihood Estimation: Multivariate Statistics

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

Maximum Likelihood Estimation: Density Estimation and Bernoulli Model

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