Maximum Likelihood Estimation

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Logistic Regression: Statistical Inference and Machine Learning

Covers logistic regression, likelihood function, Newton's method, and classification error 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.

Maximum Likelihood Estimation: Theory and Applications

Explores likelihood in probability, maximum likelihood estimation, and its applications in statistical inference.

Estimation and Confidence Intervals

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

Maximum Likelihood Inference

Explores maximum likelihood inference, comparing models based on likelihood ratios and demonstrating with a coin example.

Maximum Likelihood Estimation: Theory and Examples

Covers maximum likelihood estimation, including the Rao-Blackwell Theorem proof and practical examples of deriving estimators.

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

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

Generalised Linear Models: Regression with Exponential Family Responses

Covers regression with exponential family responses using Generalised Linear Models.

Statistical Estimation Methods

Covers statistical estimation methods, including maximum likelihood and Bayesian estimation.

Probabilistic Models for Linear Regression

Covers the probabilistic model for linear regression and its applications in nuclear magnetic resonance and X-ray imaging.