Markov Chains and Algorithm Applications

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Page 3 of 4

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

Explores the Bonus Malus system for insurance premiums and Markov chain transition probabilities.

Covers applied probability, stochastic processes, Markov chains, rejection sampling, and Bayesian inference methods.

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

Covers Markov chains, Monte Carlo sampling, isotropy, and the curse of dimensionality.

Explores the motivation and algorithm behind the Monte Carlo Chain method.

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

Introduces Markov chains and Metropolis Hastings algorithm in stochastic simulation.

Explores the convergence properties of Markov chains and the computation of long-run mean rewards.

Introduces continuous time Markov chains on a finite state space with exponential waiting times and jump probabilities.