A Markov chain or Markov process is a stochastic process describing a sequence of possible events in which the probability of each event depends only on...

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

examples of Markov chains and Markov processes in action. All examples are in the countable state space. For an overview of Markov chains in general state...

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...

In the mathematical theory of probability, an absorbing Markov chain is a Markov chain in which every state can reach an absorbing state. An absorbing...

A continuous-time Markov chain (CTMC) is a continuous stochastic process in which, for each state, the process will change state according to an exponential...

Markov chain geostatistics uses Markov chain spatial models, simulation algorithms and associated spatial correlation measures (e.g., transiogram) based...

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...

In mathematics, the quantum Markov chain is a reformulation of the ideas of a classical Markov chain, replacing the classical definitions of probability...

The Lempel–Ziv–Markov chain algorithm (LZMA) is an algorithm used to perform lossless data compression. It has been under development since either 1996...

distribution of a previous state. An example use of a Markov chain is Markov chain Monte Carlo, which uses the Markov property to prove that a particular method...

In the mathematical theory of random processes, the Markov chain central limit theorem has a conclusion somewhat similar in form to that of the classic...

from its connection to Markov chains, a concept developed by the Russian mathematician Andrey Markov. The "Markov" in "Markov decision process" refers...

stochastic process satisfying the Markov property is known as a Markov chain. A stochastic process has the Markov property if the conditional probability...

known as the Markov chain. He was also a strong, close to master-level, chess player. Markov and his younger brother Vladimir Andreevich Markov (1871–1897)...

stochastic matrix is a square matrix used to describe the transitions of a Markov chain. Each of its entries is a nonnegative real number representing a probability...

counting measures. The Markov chain is ergodic, so the shift example from above is a special case of the criterion. Markov chains with recurring communicating...

Markov chain is the time until the Markov chain is "close" to its steady state distribution. More precisely, a fundamental result about Markov chains...

) {\displaystyle O(a+b)} in the general one-dimensional random walk Markov chain. Some of the results mentioned above can be derived from properties of...

characterize continuous-time Markov processes. In particular, they describe how the probability of a continuous-time Markov process in a certain state changes...

Markov chain on a measurable state space is a discrete-time-homogeneous Markov chain with a measurable space as state space. The definition of Markov...

scientists. Markov processes and Markov chains are named after Andrey Markov who studied Markov chains in the early 20th century. Markov was interested...

balance in kinetics seem to be clear. A Markov process is called a reversible Markov process or reversible Markov chain if there exists a positive stationary...

mathematical theory of Markov chains, the Markov chain tree theorem is an expression for the stationary distribution of a Markov chain with finitely many...

Library of Congress Card Catalog Number 65-17394. "We may think of a Markov chain as a process that moves successively through a set of states s1, s2,...

equation Examples of Markov chains Category of Markov kernels Perrone (2024), pp. 10–11 Pavliotis, Grigorios A. (2014). "Markov Processes and the Chapman–Kolmogorov...

and Salvesen introduced a novel time-dependent rating method using the Markov Chain model. They suggested modifying the generalized linear model above for...

computationally intensive statistical methods including resampling methods, Markov chain Monte Carlo methods, local regression, kernel density estimation, artificial...

be used as test cases. Markov chains are an efficient way to handle Model-based Testing. Test models realized with Markov chains can be understood as a...

However, with the advent of powerful computers and new algorithms like Markov chain Monte Carlo, Bayesian methods have seen increasing use within statistics...