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.
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.
Covers the properties of the exponential family and the estimation of parameters.
Covers confidence intervals, hypothesis tests, standard errors, statistical models, likelihood, Bayesian inference, ROC curve, Pearson statistic, goodness of fit tests, and power of tests.
