Covers moments, variance, and expected values in probability and statistics, including the distribution of tokens in a product.
Covers continuous random variables, probability density functions, and distributions, with practical examples.
Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.
Covers inequalities, joint Gaussian distribution, risk estimation, and classification method testing in probability and statistics.
Covers confidence intervals, hypothesis tests, standard errors, statistical models, likelihood, Bayesian inference, ROC curve, Pearson statistic, goodness of fit tests, and power of tests.
