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Lecture
Continuous-Time Markov Chains: Reversible Chains
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Continuous-Time Markov Chains: Reversible Chains
Covers continuous-time Markov chains, focusing on reversible chains and their properties.
Continuous-Time Markov Chains: Reversible Chains
Covers Mod.7 on continuous-time Markov chains, focusing on reversible chains and their applications in communication systems.
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.
Markov Chains: Ergodicity and Stationary Distribution
Explores ergodicity and stationary distribution in Markov chains, emphasizing convergence properties and unique distributions.
Stochastic Models: Absorbing Markov Chains Examples
Covers examples of absorbing Markov chains in discrete time.
Theory of MCMC
Covers the theory of Markov Chain Monte Carlo (MCMC) sampling and discusses convergence conditions, transition matrix choice, and target distribution evolution.
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: Reversibility & Convergence
Covers Markov chains, focusing on reversibility, convergence, ergodicity, and applications.
Markov Chains: Ergodic Chains Examples
Covers stochastic models for communications, focusing on discrete-time Markov chains.
