Maximum Likelihood Estimation: Theory and Applications

Related lectures (29)

Page 1 of 3

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

Covers t-tests, p-values calculation, and comparison of coefficients.

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

Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.

Covers the normal distribution, including its properties and calculations.

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

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

Covers p-quantile, normal approximation, joint distributions, and exponential families in probability and statistics.

Provides an overview of Generalized Linear Models, focusing on logistic and Poisson regression models, and their implementation in R.

Explores independence and conditional probability in probability and statistics, with examples illustrating the concepts and practical applications.