Lectures related to Markov Chain Games

Bonus Malus System: Transition Probabilities

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

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

Markov Chains: Theory and Applications

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

Probability & Stochastic Processes

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

Hidden Markov Models: Primer

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

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.

Lindblad equation

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

Markov Chains: Properties and Expectations

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

Expected Number of Visits in State

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

Birth & Death Chains: Analysis & Probabilities

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

Stochastic Models: Absorbing Markov Chains Examples

Covers examples of absorbing Markov chains in discrete time.

Introduction to Inference

Covers the basics of probability theory, random variables, joint probability, and inference.

Sunny Rainy Source: Markov Model

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

Applied Probability & Stochastic Processes

Covers applied probability, Markov chains, and stochastic processes, including transition matrices, eigenvalues, and communication classes.

NISQ and IBM Q

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

Probability Inequalities

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

Markov Chains: Introduction and Properties

Covers the introduction and properties of Markov chains, including transition matrices and stochastic processes.

Markov Chains: Definitions and State Probabilities

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

Random Walks: Return Probabilities in Lattice Dimensions

Covers random walks on a lattice, focusing on return probabilities and their dependence on dimensionality.