Modelling Stochastic Communications: Reversible Discrete-Time Markov Chains

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Explores convergence results for periodic case reversibility in Markov chains, covering irreducible chains, positive recurrence, reversible processes, and random walks on finite graphs.

Discrete-Time Markov Chains: Definitions

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

Markov Chains: Absorbing Classes

Explores Markov chains with absorbing classes through exercises on transition matrices and expected values.

Markov Chain Games

Explores Markov chain games, hitting probabilities, and expected hitting times in a target set.

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.

Continuous-Time Markov Chains: Definitions and State Probabilities

Covers definitions and state probabilities of continuous-time Markov chains for communications.

Applied Probability & Stochastic Processes

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

Markov Chains: Applications and Coupled Chains

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

Discrete-Time Markov Chains: Definitions

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

Markov Chains: State Classification

Covers the classification of states in discrete-time Markov chains and explores the concept of periodicity.

Stochastic Processes: Markov Chains

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

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