In probability theory, the mixing time of a Markov chain is the time until the Markov chain is "close" to its steady state distribution. More precisely...

In probability theory and statistics, a Markov chain or Markov process is a stochastic process describing a sequence of possible events in which the probability...

Gauss–Markov theorem Gauss–Markov process Markov blanket Markov boundary Markov chain Markov chain central limit theorem Additive Markov chain Markov additive...

In probability, a discrete-time Markov chain (DTMC) is a sequence of random variables, known as a stochastic process, in which the value of the next variable...

Markov Chains and Mixing Times is a book on Markov chain mixing times. The second edition was written by David A. Levin, and Yuval Peres. Elizabeth Wilmer...

In statistics, Markov chain Monte Carlo (MCMC) is a class of algorithms used to draw samples from a probability distribution. Given a probability distribution...

conductance is a parameter of a Markov chain that is closely tied to its mixing time, that is, how rapidly the chain converges to its stationary distribution...

process of mixing Markov chain mixing time, the time to achieve a level of homogeneity in the probability distribution of a state in a Markov process This...

A hidden Markov model (HMM) is a Markov model in which the observations are dependent on a latent (or hidden) Markov process (referred to as X {\displaystyle...

recapture Markov additive process Markov blanket Markov chain Markov chain geostatistics Markov chain mixing time Markov chain Monte Carlo Markov decision...

walk Markov chain Examples of Markov chains Detailed balance Markov property Hidden Markov model Maximum-entropy Markov model Markov chain mixing time Markov...

The Lempel–Ziv–Markov chain algorithm (LZMA) is an algorithm used to perform lossless data compression. It has been used in the 7z format of the 7-Zip...

statistics and statistical physics, the Metropolis–Hastings algorithm is a Markov chain Monte Carlo (MCMC) method for obtaining a sequence of random samples...

seven, in the precise sense of variation distance described in Markov chain mixing time; of course, you would need more shuffles if your shuffling technique...

The MIT Press. pp. 501–40. ISBN 0-262-06141-4. Sack, Harald (2022-06-14). "Andrey Markov and the Markov Chains". SciHi Blog. Retrieved 2017-11-23. v t e...

probability measure on the set of subshifts. For example, consider the Markov chain given on the left on the states A , B 1 , B 2 {\displaystyle A,B_{1}...

these cubes. By using the theory of rapidly mixing Markov chains, they show that it takes a polynomial time for the random walk to settle down to being...

stationary Markov process is β-mixing if and only if it is an aperiodic recurrent Harris chain. The β-mixing coefficients are always bigger than the α-mixing ones...

time. A stronger concept than ergodicity is that of mixing, which aims to mathematically describe the common-sense notions of mixing, such as mixing drinks...

the walk is nearly uniformly distributed? That is, what is the Markov chain mixing time? Examples of problems studied in reconfiguration include: Games...

Dynamic Markov compression (DMC) is a lossless data compression algorithm developed by Gordon Cormack and Nigel Horspool. It uses predictive arithmetic...

In statistics, Gibbs sampling or a Gibbs sampler is a Markov chain Monte Carlo (MCMC) algorithm for sampling from a specified multivariate probability...

Markov additive process Markov blanket / Bay Markov chain mixing time / (L:D) Markov decision process Markov information source Markov kernel Markov logic...

Metropolis-adjusted Langevin algorithm (MALA) or Langevin Monte Carlo (LMC) is a Markov chain Monte Carlo (MCMC) method for obtaining random samples – sequences of...

Oleg Markov (Belarusian: Олег Маркаў, born 8 May 1996) is a professional Australian rules footballer who plays for the Collingwood Football Club in the...

(MC³) improves the mixing of Markov chains in presence of multiple local peaks in the posterior density. It runs multiple (m) chains in parallel, each...

theory and statistics, diffusion processes are a class of continuous-time Markov process with almost surely continuous sample paths. Diffusion process...

bioinformatics Margin Markov chain geostatistics Markov chain Monte Carlo (MCMC) Markov information source Markov logic network Markov model Markov random field...

programming in fixed dimensions the path coupling method for proving mixing of Markov chains (with Russ Bubley) complexity of counting constraint satisfaction...

Vladimir Markov, who is constantly belittled and dismissed by Yu as incompetent for being unable to suppress the insurgency. Shin and Markov gain a grudging...