Lectures related to Consistency of Maximum Likelihood Estimation

Explores bias, variance, unbiased estimators, and confidence intervals in statistical estimation.

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

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

Entropy and the Second Law of Thermodynamics

Covers entropy, its definition, and its implications in thermodynamics.

Estimation and Confidence Intervals

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

Statistical Models and Parameter Estimation

Explores statistical models, parameter estimation, and sampling distributions in probability and statistics.

Generalised Linear Models: Regression with Exponential Family Responses

Covers regression with exponential family responses using Generalised Linear Models.

Estimation: Linear Estimator

Explores linear estimation, optimal criteria, and the orthogonality principle for good choices in estimation.

Maximum Likelihood Estimation

Introduces maximum likelihood estimation for statistical parameter estimation, covering bias, variance, and mean squared error.

Maximum Likelihood Estimation: Properties and Applications

Covers Maximum Likelihood Estimation properties, applications, and assumptions, providing a comprehensive understanding of MLE concepts and their practical implications.

Bias, Variance, Consistency, EMV

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

Maximum Likelihood, MSE, Fisher Information, Cramér-Rao Bound

Explains maximum likelihood estimation, MSE, Fisher information, and Cramér-Rao bound in statistical inference.

Sampling Distributions: Theory and Applications

Explores sampling distributions, estimators' properties, and statistical measures for data science applications.

Entropy and Sampling Theory

Explores entropy, exponential family, sampling theory, and statistical inference from samples.

Boltzmann Machine

Introduces the Boltzmann Machine, covering expectation consistency, data clustering, and probability distribution functions.

Monte Carlo: Optimization and Estimation

Explores optimization and estimation in Monte Carlo methods, emphasizing Bayes-optimal groups and estimators.

Sampling Distributions: Estimators and Variance

Covers estimation of parameters, MSE, Fisher information, and the Rao-Blackwell Theorem.

Elements of Statistics: Probability, Distributions, and Estimation

Covers probability theory, distributions, and estimation in statistics, emphasizing accuracy, precision, and resolution of measurements.

Maximum Likelihood Estimation

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

Detection & Estimation

Covers the fundamentals of detection and estimation theory, focusing on mean-squared error and hypothesis testing.