Lectures related to Continuous-Time Markov Chains: Birth and Death Processes

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

Introduces Hidden Markov Models, explaining the basic problems and algorithms like Forward-Backward, Viterbi, and Baum-Welch, with a focus on Expectation-Maximization.

Probability & Stochastic Processes

Covers applied probability, stochastic processes, Markov chains, rejection sampling, and Bayesian inference methods.

Markov Chains: Ergodicity and Stationary Distribution

Explores ergodicity and stationary distribution in Markov chains, emphasizing convergence properties and unique distributions.

Stochastic Models for Communications: Discrete-Time Markov Chains - Absorption Time

Discusses discrete-time Markov chains and absorption time in communication systems.

Stochastic Models for Communications: Discrete-Time Markov Chains - First Passage Time

Explores discrete-time Markov chains, emphasizing first passage time probabilities and minimal solutions.

Stochastic Models for Communications

Covers stochastic models for communications, focusing on random variables, Markov chains, Poisson processes, and probability calculations.

Birth & Death Chains: Analysis & Probabilities

Explores birth and death chains, hitting probabilities, and expected game durations in Markov chains.

Bonus Malus System: Transition Probabilities

Explores the Bonus Malus system for insurance premiums and Markov chain transition probabilities.

Sunny Rainy Source: Markov Model

Explores a first-order Markov model using a sunny-rainy source example, demonstrating how past events influence future outcomes.

Hitting Times: Examples

Explores hitting times in Markov chains through various examples and scenarios.

Expected Number of Visits in State

Covers the criterion for recurrence in infinite chains based on the expected number of visits in a state.

Stochastic Models: Absorbing Markov Chains Examples

Covers examples of absorbing Markov chains in discrete time.

Markov Chains: Properties and Expectations

Explores Markov chains' properties, expectations, and recurrence in Poisson processes.

Lindblad equation

Covers the interpretation of the Lindblad equation and its unitary part in quantum gases.

Stochastic Models for Communications

Covers mathematical tools for communication systems and data science, including information theory and signal processing.

Markov Chain Games

Explores Markov chain games, hitting probabilities, and expected hitting times in a target set.

Conditional Probability: Prediction Decomposition

Explores conditional probability, Bayes' theorem, and prediction decomposition for informed decision-making.

Discrete-Time Markov Chains: Definitions

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

Statistical Analysis: Boxplot and Normal Distribution

Introduces statistical analysis concepts like boxplot and normal distribution using real data examples.