Dependability Evaluation in Industrial Automation

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Page 3 of 4

Explores Markov chains, Ising Model, Metropolis algorithm, and Glauber dynamics.

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

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

Covers the interpretation of the Lindblad equation and its unitary part in quantum gases.

Explores recurrence and transience in Markov chains, discussing the strong Markov property and state classifications.

Explores equilibrium in Markov Chains, covering invariant distributions, properties determination, and practical applications.

Covers stochastic models for communications, focusing on discrete-time Markov chains.

Introduces risk analysis methods like PHA, Checklist, and FMECA, emphasizing systematic evaluation and failure analysis.

Explores the Failure Mode Analysis method, emphasizing the importance of identifying potential failures and its widespread use in various industries.

Explores the decomposition of Markov chains into communicating classes and the behavior of long-run averages.