Stochastic Simulation: Markov Chains and Transition Matrices

Related lectures (33)

Introduces Markov chains and Metropolis Hastings algorithm in stochastic simulation.

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.

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

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

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

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

Discusses discrete-time Markov chains and absorption time in communication systems.

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

Covers examples of absorbing Markov chains in discrete time.

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

Covers parametric signal models and practical Matlab applications for Markov chains and AutoRegressive processes.

Discusses the residue theorem and its applications in calculating complex integrals.

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