Stochastic Simulation: Markov Chains and Transition Matrices

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Introduces Markov chains and Metropolis Hastings algorithm in stochastic simulation.

Covers computational methods for molecular systems at finite temperature, emphasizing stochastic sampling and time evolution simulations.

Covers the theory of Markov chains, focusing on reversible chains and detailed balance.

Covers stochastic models for communications, focusing on random variables, Markov chains, Poisson processes, and probability calculations.

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

Introduces the Potts model, a generalization of the Ising model used in statistical mechanics to study phase transitions.

Explores ergodicity and stationary distribution in Markov chains, emphasizing convergence properties and unique distributions.

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

Covers Markov chains and their applications in algorithms, focusing on Markov Chain Monte Carlo sampling and the Metropolis-Hastings algorithm.

Explores rejection sampling for generating sample values from a target distribution, along with Bayesian inference using MCMC.