Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.
Delves into probability, statistics, paradoxes, and random variables, showcasing their real-world applications and properties.
Covers jacamar data analysis, smoking data models, and challenges with log-linear models in visual impairment data.
Covers Generalized Linear Models, likelihood, deviance, link functions, sampling methods, Poisson regression, over-dispersion, and alternative regression models.
Explores maximum likelihood inference, comparing models based on likelihood ratios and demonstrating with a coin example.
Explores latent variable models, EM algorithm, and Jensen's inequality in statistical modeling.
