Lectures related to Markov Chains and Applications

Markov Chains: Applications and Analysis

Explores Markov chains, focusing on the coloring problem and algorithm analysis.

Markov Chains and Algorithm Applications

Covers the application of Markov chains and algorithms for function optimization and graph colorings.

MCMC Examples and Error Estimation

Covers Markov Chain Monte Carlo examples and error estimation methods.

Markov Chains: Applications and Sampling Methods

Covers the basics of Markov chains and their algorithmic applications.

Markov Chains and Algo Applications

Covers Markov chains, Metropolis algorithm, Glauber dynamics, and heat bath dynamics.

Markov Chains and Algorithm Applications

Covers the fundamentals of Markov chains and their applications in algorithms, focusing on proper coloring and the Metropolis algorithm.

Markov Chains: Theory and Applications

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

Lindblad equation

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

Markov Chains and Algorithm Applications

Covers Markov chains and their applications in algorithms, focusing on Markov Chain Monte Carlo sampling and the Metropolis-Hastings algorithm.

Markov Chains: Introduction and Properties

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

Markov Chains: Applications and Coupled Chains

Covers Markov chains, coupled chains, and their applications, emphasizing the importance of irreducibility.

Applied Probability & Stochastic Processes

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

Markov Chains and Algorithm Applications

Explores Markov chains and algorithm applications, including exact simulation and Propp-Wilson algorithms.

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.

Markov Chains: Ergodicity and Stationary Distribution

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

Markov Chains and Applications

Explores Markov chains, their properties, and algorithmic applications, emphasizing information quantification and state monotonicity.

Markov Chains: Recurrence and Transience

Explores first passage times, strong Markov property, and state recurrence/transience in Markov chains.

Quantum Entropy: Markov Chains and Bell States

Explores quantum entropy in Markov chains and Bell states, emphasizing entanglement.

Probability & Stochastic Processes

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

Markov Chains and Applications

Explores Markov chains and their applications in algorithms, focusing on user impatience and faithful sample generation.