Explores probability inequalities, convergence types, and moment generating functions for distribution approximation.
Covers Simpson's paradox, probability distributions, and real-life examples in probability and statistics.
Explores convergence in probability, concentration inequalities, laws of large numbers, and properties of distributions.
Covers the fundamental concepts of probability and statistics, including interesting results, standard model, image processing, probability spaces, and statistical testing.
Covers random variables, expectation, and distributions in probability spaces.
Covers p-quantile, normal approximation, joint distributions, and exponential families in probability and statistics.
Explores continuous random variables, density functions, joint variables, independence, and conditional densities.
Delves into probability, statistics, paradoxes, and random variables, showcasing their real-world applications and properties.
Introduces key statistical concepts through practical exercises and MATLAB applications.
Covers fundamental concepts in probability and statistics, including distributions, properties, and expectations of random variables.
Introduces key concepts in probability and statistics, such as events, Venn diagrams, and conditional probability.
Covers probability, random variables, expectation, GLMs, hypothesis testing, and Bayesian statistics with practical examples.
