Markov Chains: Introduction and Properties

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Page 3 of 4

Explores communicating classes in Markov chains, distinguishing between transient and recurrent classes, and delves into the properties of these classes.

Markov Chains: Definitions and State Probabilities

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

Stochastic Models: Absorbing Markov Chains Examples

Covers examples of absorbing Markov chains in discrete time.

MCMC Examples and Error Estimation

Covers Markov Chain Monte Carlo examples and error estimation methods.

NISQ and IBM Q

Explores NISQ devices and IBM Q, covering noisy quantum circuits, qubit technologies, and quantum algorithm development.

Sunny Rainy Source: Markov Model

Explores a first-order Markov model using a sunny-rainy source example, demonstrating how past events influence future outcomes.

Markov Chains: Recurrence and Transience

Explores first passage times, strong Markov property, and state recurrence/transience in Markov chains.

Invariant Measures: Properties and Applications

Covers the concept of invariant measures in Markov chains and their role in analyzing irreducible recurrent processes.

Markov Chains and Algorithm Applications

Explores Markov chains and algorithm applications, including exact simulation and Propp-Wilson algorithms.

Markov Chains: Applications and Coupled Chains

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