Stationary distribution

Related lectures (29)

Page 3 of 3

Explores optimization techniques like Metropolis-Hastings and Simulated Annealing through Markov chains and stationary distributions.

Explores Markov chain convergence, spectral gap, and acceleration techniques for faster convergence.

Explores positive recurrence and invariant distributions in Markov chains, discussing their relationship and implications.

Explores Markov chains and stationary distributions, emphasizing the importance of identifying them for improving convergence.

Explores reversibility in Markov chains and its impact on the stationary distribution, highlighting the complexity of non-reversible chains.

Explores Markov Chain Monte Carlo for sampling high-dimensional distributions and optimizing functions using the Metropolis-Hastings algorithm.

Covers Markov chains, random variables, independence, characteristic functions, and queueing theory.

Explores time reversal in stationary Markov chains and the concept of detailed balance conditions.

Explores the generation of stochastic processes, including Gaussian processes, Markov processes, Poisson processes, and circulant embedding.