Markov model

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Markov model

(probability, simulation)
A model or simulation based on Markov chains.
References in periodicals archive ?
Including over 7,000 Hidden Markov Models, it provides structural assignments to protein sequences and enables researchers to detect broader and more distant evolutionary relationships through sophisticated remote homology detection.
Further, Bayesian networks are upgraded to Bayesian logic programs, hidden Markov models to logical hidden Markov models, and Markov decision process to Markov decision programs.
It executes some of the most sensitive and selective algorithms, such as Smith-Waterman and Hidden Markov Models, for biological data searches, analysis and gene finding at speeds thousands of times faster than the latest generation microprocessor.
Topics include using volcanic processes as sources of statistical data, creating models for assessment, determining the probability of explosive eruption, using extreme value methods for historical modeling and spatial distribution, applying the Gutenberg-Richter Law, calculating stationary and non-stationary time series and finding new approaches, employing wavelet-based hidden Markov models, reading a coupled conduit and eruption column model, evaluating transient models of conduit flows, using multiple parameters in studies of processes, inverting tephra fallout and using probabilistic models for tephra dispersion.
DeCypher technology provides one of the fastest bioinformatics solutions for sensitive but computationally intensive algorithms, including BLAST, Smith-Waterman, Hidden Markov Models, FrameSearch, and many others.
Topics include ion-acoustic structural turbulence in low-temperature magnetized plasma, low-frequency structural plasma turbulence in stellarators, new possibilities for the mathematical modeling turbulent transport processes in plasma, multi-fractal statistics of edge plasma turbulence, Fractionally stale distribution, and hidden Markov models of plasma turbulence.
He has worked on a number of topics in machine learning, including neural networks, mixture models, decision trees, hidden Markov models, Boltzmann machines and Bayesian networks.
Topics include training digital monsters to fight in the real world, Orwellian state machines, populating large worlds using limited resources, and an introduction to hidden Markov models.
A single GeneMatcher contains more than 6,000 custom processors, programmable to execute a full range of genetic comparison algorithms such as BLAST, Smith-Waterman, hidden Markov models, frame search and profile search.
Other topics include exploiting geometry for support vector machine indexing, Markov models for identification of significant episodes, Gaussian processes for active data mining of spatial aggregates, and iterative mining for rules with constrained antecedents.
A single GeneMatcher contains more than 6,000 custom processors, programmable to execute a full range of genetic comparison algorithms such as Smith-Waterman, hidden Markov models, frame search and profile search.
This book introduces block diagrams, fault trees, and Markov models for graphically representing the reliability of a system, and describes the various types of sensors, logic solvers, actuators, and valves available for safety instrumented systems.