Explores continuous random variables, density functions, joint variables, independence, and conditional densities.
Covers p-quantile, normal approximation, joint distributions, and exponential families in probability and statistics.
Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.
Covers the fundamental concepts of probability and statistics, including interesting results, standard model, image processing, probability spaces, and statistical testing.
Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Covers jacamar data analysis, smoking data models, and challenges with log-linear models in visual impairment data.
Covers Simpson's paradox, probability distributions, and real-life examples in probability and statistics.
Covers binary classification, hypothesis testing, likelihood ratio tests, and decision rules.
Delves into probability, statistics, paradoxes, and random variables, showcasing their real-world applications and properties.
Explores dependence, correlation, and conditional expectations in probability and statistics, highlighting their significance and limitations.
