Lectures related to Markov Chains: Homogeneous Processes and Stationary Distributions

Limiting Distribution and Ergodic Theorem

Explores limiting distribution in Markov chains and the implications of ergodicity and aperiodicity on stationary distributions.

Probability & Stochastic Processes

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

Coupling of Markov Chains: Ergodic Theorem

Explores the coupling of Markov chains and the proof of the ergodic theorem, emphasizing distribution convergence and chain properties.

Markov Chains: Transition Probabilities

Explores Markov chains, transition matrices, distribution, and random walks.

Theory of MCMC

Covers the theory of Markov Chain Monte Carlo (MCMC) sampling and discusses convergence conditions, transition matrix choice, and target distribution evolution.

Markov Chains: Ergodicity and Stationary Distribution

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

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.

Ergodic Theory: Markov Chains

Explores ergodic theory in Markov chains, discussing irreducibility and unique stationary distributions.

Stochastic Processes: Markov Chains

Covers stochastic processes, focusing on Markov chains and their applications in real-world scenarios.

Markov Chains: PageRank Algorithm

Explores the PageRank algorithm within Markov chains, emphasizing ergodicity and convergence for web page ranking.

Continuous-Time Markov Chains: Reversible Chains

Covers reversible continuous-time Markov chains and their properties.

Markov Chains: Ergodic Chains Examples

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

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.

Invariant Distributions: Markov Chains

Explores invariant distributions for Markov Chains, emphasizing uniqueness and implications in communicating classes.

Elements of Statistics: Probability, Distributions, and Estimation

Covers probability theory, distributions, and estimation in statistics, emphasizing accuracy, precision, and resolution of measurements.

Levy Flights and Central Limit Theorem

Covers Levy flights, Central Limit Theorem, and Mesoscopic Master Equation with transition rates in an assurance system.

Markov Chains: Applications and Sampling Methods

Covers the basics of Markov chains and their algorithmic applications.

Lower Bound on Total Variation Distance

Explores the lower bound on total variation distance in Markov chains and its implications on mixing time.

Applied Probability & Stochastic Processes

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