Maximum Likelihood Inference

Related lectures (30)

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

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

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

Covers the basics of probability theory, random variables, joint probability, and inference.

Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.

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

Covers the properties of the exponential family and the estimation of parameters.

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

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

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