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Lecture
Markov Chains: Ergodic Chains Examples
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Markov Chains: Ergodic Chains Examples
Covers stochastic models for communications, focusing on discrete-time Markov chains.
Markov Chains: Ergodicity and Stationary Distribution
Explores ergodicity and stationary distribution in Markov chains, emphasizing convergence properties and unique distributions.
Ergodic Theorem: Proof and Applications
Explains the proof of the ergodic theorem and the concept of positive-recurrence in Markov chains.
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: Ergodic Chains Examples
Covers stochastic models for communications, focusing on discrete-time Markov chains.
Markov Chains: PageRank Algorithm
Explores the PageRank algorithm within Markov chains, emphasizing ergodicity and convergence for web page ranking.
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.
Continuous-Time Markov Chains: Reversible Chains
Covers continuous-time Markov chains, focusing on reversible chains and their properties.
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.
Continuous-Time Markov Chains: Reversible Chains
Covers Mod.7 on continuous-time Markov chains, focusing on reversible chains and their applications in communication systems.
