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...

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

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...

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

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

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

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

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

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

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}...

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...

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...

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

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...

In numerical methods for stochastic differential equations, the Markov chain approximation method (MCAM) belongs to the several numerical (schemes) approaches...

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

simplest Markov model is the Markov chain. It models the state of a system with a random variable that changes through time. In this context, the Markov property...

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...

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

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

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...

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...

probability theory, a telescoping Markov chain (TMC) is a vector-valued stochastic process that satisfies a Markov property and admits a hierarchical...

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

of jobs to the queue. Markov chains with generator matrices or block matrices of this form are called M/G/1 type Markov chains, a term coined by Marcel...

boundary were coined by Judea Pearl in 1988. A Markov blanket can be constituted by a set of Markov chains. A Markov blanket of a random variable Y {\displaystyle...

{\displaystyle S} in the Markov chain to letters in the alphabet Γ {\displaystyle \Gamma } . A unifilar Markov source is a Markov source for which the values...

) {\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...