Covers the multinomial distribution, joint density, marginal distribution, and conditional distribution.
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 p-quantile, normal approximation, joint distributions, and exponential families in probability and statistics.
Introduces key concepts in probability and statistics, covering random experiments, events, intersections, unions, and more.
Explores dependence in random vectors, covering joint density, conditional independence, covariance, and moment generating functions.
