Explains key concepts in probability, including conditional probability, independence, and random variables, with practical examples to illustrate their applications.
Covers the fundamental concepts of probability and statistics, including interesting results, standard model, image processing, probability spaces, and statistical testing.
Covers quantization of probability distributions, statistical k-means clustering, mean estimation, robust clustering methods, and open research questions.
Covers Markov processes, transition densities, and distribution conditional on information, discussing classification of states and stationary distributions.
