Lectures related to Naive Bayes: Gaussian Discriminant Analysis

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

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

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

Introduces Maximum Likelihood Estimation in econometrics, covering principles, properties, applications, and specification tests.

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

Explores the Cramér-Rao bound, hypothesis testing, and optimality in statistical theory.

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

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

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

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

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

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

Explores generative learning algorithms, decision rules, and Gaussian distribution properties in machine learning.

Covers the Bayes discriminant rule for allocating individuals to populations based on measurements and prior probabilities.

Explores modeling neurobiological signals with Markov Chains, focusing on parameter estimation and data classification.

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

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

Covers jacamar data analysis, smoking data models, and challenges with log-linear models in visual impairment data.

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

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