after the Russian mathematician Andrey Markov. The term strong Markov property is similar to the Markov property, except that the meaning of "present"...

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...

probability, a Markov random field (MRF), Markov network or undirected graphical model is a set of random variables having a Markov property described by...

the underlying structure of state transitions that still follow the Markov property. The process is called a "decision process" because it involves making...

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...

not on the events that occurred before it (that is, it assumes the Markov property). Generally, this assumption enables reasoning and computation with...

decision process Markov's inequality Markov brothers' inequality Markov information source Markov network Markov number Markov property Markov process Stochastic...

last equation is in the form of a local Markov property. Measures with this property are sometimes called Markov random fields. More strongly, the converse...

is a Bayesian network with respect to G if it satisfies the local Markov property: each variable is conditionally independent of its non-descendants...

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...

Andrey Markov: A Markov chain or Markov process, a stochastic model describing a sequence of possible events The Markov property, the memoryless property of...

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

Michael O. Rabin (1958). A Markov property P of finitely presentable groups is one for which: P is an abstract property, that is, P is preserved under...

have a number of nice properties, which include sample and Feller continuity; the Markov property; the strong Markov property; the existence of an infinitesimal...

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...

ergodic theory, a Markov operator is an operator on a certain function space that conserves the mass (the so-called Markov property). If the underlying...

contains 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...

Markov Markov chain, a mathematical process useful for statistical modeling Markov random field, a set of random variables having a Markov property described...

rules. Although the concept is named after Andrey Markov due to its reliance on the Markov property—the idea that only the current state matters—the strategy...

their mathematical properties, stochastic processes can be grouped into various categories, which include random walks, martingales, Markov processes, Lévy...

hit β. This property is often referred to as the Markov property of loop-erased random walk (though the relation to the usual Markov property is somewhat...

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

but here we have only the weaker assumption that the process has the Markov property; and g {\textstyle g} is some (measurable) real-valued function for...

intelligence, which employ Markov networks, and Markov logic networks. The Gibbs measure is also the unique measure that has the property of maximizing the entropy...

statistics, a hidden Markov random field is a generalization of a hidden Markov model. Instead of having an underlying Markov chain, hidden Markov random fields...

represented as a Markov shift. The appellation 'Markov' is appropriate because the resulting dynamics of the system obeys the Markov property. The Markov partition...

a trivial matter to show that a Gibbs random field satisfies every Markov property. As an example of this fact, see the following: In the image to the...

job arrivals to a queue over time. If a process has the Markov property, it is said to be a Markov counting process. Intensity of counting processes Poisson...

Ornstein–Uhlenbeck process. Gauss–Markov processes obey Langevin equations. Every Gauss–Markov process X(t) possesses the three following properties: If h(t) is a non-zero...

distribution of the process at time t. It is a corollary of the strong Markov property of Brownian motion. If ( W ( t ) : t ≥ 0 ) {\displaystyle (W(t):t\geq...