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 Hidden Markov Models, explaining the basic problems and algorithms like Forward-Backward, Viterbi, and Baum-Welch, with a focus on Expectation-Maximization.
Covers p-quantile, normal approximation, joint distributions, and exponential families in probability and statistics.
Explores a first-order Markov model using a sunny-rainy source example, demonstrating how past events influence future outcomes.
Covers fundamental concepts in probability and statistics, emphasizing data analysis techniques and statistical modeling.
