Maximum Likelihood: Inference and Model Comparison

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Covers the concepts of Demystification, Estimation, and Bayesian Inference in the context of Bayesian statistics.

Covers demousing, estimation, Bayesian inference, likelihood, AWGN, and more.

Explores sparse learning of chemical reaction networks from trajectory data using data-based methods and learning approaches.

Covers variational inference and neural networks for classification tasks.

Explores logistic regression model interpretation, parameter estimation, and model comparison using likelihood ratio tests.

Explores the impact of a binary covariate on binary responses using 2x2 tables.

Explores model selection using AIC and BIC criteria, addressing different questions and the importance of sparsity in selecting the best model.

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

Explores logistic regression for binary classification, covering probability modeling, optimization methods, and regularization techniques.

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