Lectures related to Stochastic Models: Absorbing Markov Chains Examples

Introduces key concepts in probability and random variables, covering statistics, distributions, and covariance.

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.

Covers free fields and probabilistic functions, focusing on random variables and their properties.

Covers the theory and applications of Markov chains in modeling random phenomena and decision-making under uncertainty.

Covers stochastic processes in continuous time, including probability functions and stock processes.

Explores PDF forms, statistics, boxplots, density curves, and data analysis methods.

Covers the definitions and state probabilities of discrete-time Markov chains.