a Markov information source, or simply, a Markov source, is an information source whose underlying dynamics are given by a stationary finite Markov chain...

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

method Markov chain geostatistics Markov chain mixing time Markov chain tree theorem Markov decision process Markov information source Markov odometer...

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

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...

\dots ,X_{n-1},X_{n})}{n+1}}.} Markov information source Asymptotic equipartition property Robert B. Ash, Information Theory, (1965) Dover Publications...

used for feature selection in machine learning. Information source (mathematics) Markov information source Asymptotic equipartition property Maximal entropy...

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

Markov decision process (MDP), also called a stochastic dynamic program or stochastic control problem, is a model for sequential decision making when outcomes...

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...

observed events. This is done especially in the context of Markov information sources and hidden Markov models (HMM). The algorithm has found universal application...

Science and Technology - Volume 14: Very Large Data Base Systems to Zero-Memory and Markov Information Source. Marcel Dekker Inc. ISBN 978-0-8247-2214-2....

that for the output of Markov information sources, Kolmogorov complexity is related to the entropy of the information source. More precisely, the Kolmogorov...

maximum-entropy Markov model (MEMM), or conditional Markov model (CMM), is a graphical model for sequence labeling that combines features of hidden Markov models...

common way to define entropy for text is based on the Markov model of text. For an order-0 source (each character is selected independent of the last characters)...

computer science, a Markov algorithm is a string rewriting system that uses grammar-like rules to operate on strings of symbols. Markov algorithms have been...

process Markov information source Markov kernel Markov logic network Markov model Markov network Markov process Markov property Markov random field Markov renewal...

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

Comics. The first Terra, Tara Markov, joins the Teen Titans as a double agent for the supervillain Deathstroke. Markov was created by Marv Wolfman and...

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...

mixtures, Bayesian networks, neural networks, radial basis functions, hidden Markov models, stochastic context-free grammars, reduced rank regressions, Boltzmann...

of information theory include source coding, algorithmic complexity theory, algorithmic information theory and information-theoretic security. Applications...

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

Technology: Volume 14 - Very Large Data Base Systems to Zero-Memory and Markov Information Source. CRC Press. pp. 1–18. ISBN 9780824722142. Gerritsen, Rob; Morgan...

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

Monroe D.; Varadhan, SR Srinivasa (1983). "Asymptotic evaluation of certain Markov process expectations for large time. IV". Communications on Pure and Applied...

_{0}\right)} , and because the Kalman filter describes a Markov process, all relevant information from previous observations is contained in the current...

hierarchical models using Markov chain Monte Carlo developed by Martyn Plummer. It is similar to WinBUGS KNIME – An open source analytics platform built...

is equal to the mutual information, maximized over all input distributions. Discriminative training procedures for hidden Markov models have been proposed...

exploration–exploitation dilemma. The environment is typically stated in the form of a Markov decision process (MDP), as many reinforcement learning algorithms use dynamic...