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In theoretical 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 shown to be Turing-complete, which means that they are suitable as a general model of computation and can represent any mathematical expression from its simple notation. Markov algorithms are named after the Soviet mathematician Andrey Markov, Jr.
Refal is a programming language based on Markov algorithms.
science, a Markovalgorithm is a string rewriting system that uses grammar-like rules to operate on strings of symbols. Markovalgorithms have been shown...
In statistics, Markov chain Monte Carlo (MCMC) is a class of algorithms used to draw samples from a probability distribution. Given a probability distribution...
In mathematics, a Markov decision process (MDP) is a discrete-time stochastic control process. It provides a mathematical framework for modeling decision...
likelihood. For linear chain HMMs, the Baum–Welch algorithm can be used to estimate the parameters. Hidden Markov models are known for their applications to...
This is done especially in the context of Markov information sources and hidden Markov models (HMM). The algorithm has found universal application in decoding...
A Markov chain or Markov process is a stochastic model describing a sequence of possible events in which the probability of each event depends only on...
The forward algorithm, in the context of a hidden Markov model (HMM), is used to calculate a 'belief state': the probability of a state at a certain time...
example, the Viterbi algorithm finds the most likely sequence of spoken words given the speech audio. A Markov decision process is a Markov chain in which state...
named after the Russian mathematician Andrey Markov. The term strong Markov property is similar to the Markov property, except that the meaning of "present"...
learning algorithms is that the latter do not assume knowledge of an exact mathematical model of the Markov decision process and they target large Markov decision...
Nikita I.; Lemeire, Jan; Aliferis, Constantin F. (2013). "Algorithms for discovery of multiple Markov boundaries" (PDF). Journal of Machine Learning Research...
The Secure Hash Algorithms are a family of cryptographic hash functions published by the National Institute of Standards and Technology (NIST) as a U.S...
term gives the value of the recursive function applied to the inputs. Markovalgorithm a string rewriting system that uses grammar-like rules to operate on...
efficient algorithm for computing the throughput-delay performance for any stable system. There are 3 key results, shown below, from Lam’s Markov chain model...
(7): 424–436. doi:10.1145/359131.359136. S2CID 2509896. A.A. Markov (1954) Theory of algorithms. [Translated by Jacques J. Schorr-Kon and PST staff] Imprint...
ANT) algorithm Hammersley–Clifford theorem Harmony search Hebbian theory Hidden Markov random field Hidden semi-Markov model Hierarchical hidden Markov model...
property, Markov's inequality, Markov processes, Markov random field, Markovalgorithm etc. Andrey Markov, Jr., author of Markov's principle and Markov's rule...
Cocke–Younger–Kasami algorithm (alternatively called CYK, or CKY) is a parsing algorithm for context-free grammars published by Itiroo Sakai in 1961. The algorithm is named...
In statistics, Gibbs sampling or a Gibbs sampler is a Markov chain Monte Carlo (MCMC) algorithm for sampling from a specified multivariate probability...
a Markov information source, or simply, a Markov source, is an information source whose underlying dynamics are given by a stationary finite Markov chain...
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