Lectures related to Markov Chains: Challenges and Surprises

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

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

Sunny Rainy Source: Markov Model

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

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: Absorbing Markov Chains Examples

Covers examples of absorbing Markov chains in discrete time.

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 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.

Lindblad equation

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

Probability Inequalities

Explores probability inequalities, convergence types, and moment generating functions for distribution approximation.

Markov Chains: Theory and Applications

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

Markov Chains: Properties and Expectations

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

Law of Large Numbers: Strong Convergence

Explores the strong convergence of random variables and the normal distribution approximation in probability and statistics.

Discrete-Time Markov Chains: Definitions

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

NISQ and IBM Q

Explores NISQ devices and IBM Q, covering noisy quantum circuits, qubit technologies, and quantum algorithm development.

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

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

Markov Chain Analysis

Delves into Markov chains by analyzing a scenario with two fleas moving in opposite directions, exploring transition matrices and probabilities over time.

Markov Chains: Absorbing Classes

Explores Markov chains with absorbing classes through exercises on transition matrices and expected values.

Dependence and Correlation

Explores dependence, correlation, and conditional expectations in probability and statistics, highlighting their significance and limitations.