hidden Markov model

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hidden Markov model

[¦hid·ən ′mär·kəf ‚mäd·əl]
(mathematics)
A finite-state machine that is also a doubly stochastic process involving at least two levels of uncertainty: a random process associated with each state, and a Markov chain, which characterizes the probabilistic relationship among the states in terms of how likely one state is to follow another.
References in periodicals archive ?
A revealing introduction to hidden Markov models [online] [cited 06 January 2012].
During the time, we have designed and used different high complexity prediction methods based on Markov chains, Hidden Markov Models (HMM), Neural Networks, and also some simple methods which are very efficient for hardware implementation like the Last Value Predictor and the Two Level Predictors.
The nonlinear workbook; chaos, fractals, cellular automata, neural networks, genetic algorithms, gene expression programming, support vector machine, wavelets, hidden Markov models, Fuzzy logic.
Recently, the ARBIMON team made a major breakthrough in automating species identification using Hidden Markov Models (http://en.
The core of the most OCR systems are, algorithms based on neural networks, hidden Markov models, minimum distance classifiers or support vector.
After background chapters on important algorithms, general-purpose graphic processors, and massively threaded programming and reconfigurable computing with field-programmable gate arrays, researchers in the field present recent approaches to parallelize bioinformatics applications on a variety of parallel architectures, including pairwise and multiple sequence alignment, the basic local alignment search tool, motif finding, pattern matching, sequence assembly, hidden Markov models, proteomics, and evolutionary tree reconstruction.
Ypma and Haskes (12) expanded the work done by Cadez and Heckerman by using mixtures of Hidden Markov models.
Our system depends on simulating the biological immune system features and also uses a finite state machine called Hidden Markov models that enables us to detect viruses according to their behaviour.
Similarly, machine learning techniques like Hidden Markov Models which were used in speech recognition are now being used for protein homology prediction.
Hidden Markov Models have proved useful for many problems in the domain of molecular biology and have been applied to a variety of problems, and are particularly useful in linear sequence analysis o proteins.
The sampled speech utterance is split into distinct phonetic sounds, then Hidden Markov models are used to hypothesize boundaries between sounds, and to form probabilistic models for each possible combination.
To create a computer program to achieve this, Borodovsky employed a probabilistic mathematical model called Hidden Markov Models or HMM.