Lectures related to Invariant Measures in Markov Chains

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.

Birth and Death Chains

Explores transience and recurrence in Markov chains, emphasizing birth and death chains and random walks.

Markov Chains and Algo Applications

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

Markov Chains: Introduction and Properties

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

Markov Chains: Ergodicity and Stationary Distribution

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

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: Applications and Coupled Chains

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

Neurobiological Signals: Processing and Classification

Explores neurobiological signal processing, including spike modeling, de-noising, and data classification techniques.

Markov Chains and Algorithm Applications

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

Recurrence and Transience: Markov Chains

Explores recurrence and transience in Markov chains, discussing the strong Markov property and state classifications.

Markov Chains: Ergodic Chains Examples

Covers stochastic models for communications, focusing on discrete-time Markov chains.

Quantum Entropy: Markov Chains and Bell States

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

MCMC Examples and Error Estimation

Covers Markov Chain Monte Carlo examples and error estimation methods.

Markov Chains and Applications

Explores Markov chains, Ising Model, Metropolis algorithm, and Glauber dynamics.

Markov Chains: Theory and Applications

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

Markov Chains and Algorithm Applications

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

Markov Chains and Applications

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

Markov Chains: Ergodic Chains Examples

Covers stochastic models for communications, focusing on discrete-time Markov chains.

Markov Chains: Ergodic Chains Examples

Covers stochastic models for communications, focusing on discrete-time Markov chains.

Discrete-Time Markov Chains: Definitions

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