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

In probability theory, a Markov model is a stochastic model used to model pseudo-randomly changing systems. It is assumed that future states depend only...

term Markov assumption is used to describe a model where the Markov property is assumed to hold, such as a hidden Markov model. A Markov random field extends...

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

takes on random values over a space of functions (see Feynman integral). Several kinds of random fields exist, among them the Markov random field (MRF),...

in 1988. A Markov blanket can be constituted by a set of Markov chains. A Markov blanket of a random variable Y {\displaystyle Y} in a random variable set...

In statistics, a Gaussian random field (GRF) is a random field involving Gaussian probability density functions of the variables. A one-dimensional GRF...

rare failure region.[citation needed] Markov chain Monte Carlo methods create samples from a continuous random variable, with probability density proportional...

geostatistics Markov chain mixing time Markov chain tree theorem Markov decision process Markov information source Markov odometer Markov operator Markov random field...

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...