Markov Chains: Basics and Applications

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Page 3 of 4

Markov Chains: Definition and Examples

Covers the definition and properties of Markov chains, including transition matrix and examples.

Birth & Death Chains: Analysis & Probabilities

Explores birth and death chains, hitting probabilities, and expected game durations in Markov chains.

Stochastic Simulation: Markov Chains and Metropolis Hastings

Introduces Markov chains and Metropolis Hastings algorithm in stochastic simulation.

Markov Chain Games

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

Markov Chains: Definitions and Transitions

Explains Markov chains, transition matrices, and stationary distributions in random processes.

Stochastic Models for Communications

Covers stochastic models for binary transmission in communications systems.

Markov Chains: Simulation and Optimization

Explores Markov chains, Metropolis-Hastings, and simulation for optimization purposes, highlighting the significance of ergodicity in efficient variable simulation.

Markov Chains: Definitions and State Probabilities

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

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.