Stochastic Simulation: Markov Chains and Metropolis Hastings

Related lectures (32)

Page 3 of 4

Covers the definition and properties of Markov chains, including transition matrix and examples.

Covers the proof of the equivalence between AQ=0 and XP(s)=X.

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

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

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

Covers stochastic models for communication systems, including concepts like stochastic processes and Markov chains.

Covers Markov chains, Monte Carlo methods, low discrepancy sequences, and multidimensional integrals computation.

Covers determinantal point processes, sine-process, and their extrapolation in different spaces.

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

Covers the Markov Chain Monte Carlo method and the Metropolis-Hastings algorithm for generating samples from a target probability distribution.