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Markov models - training and evaluation of hidden Markov models. 2020

Jasleen K Grewal, and Martin Krzywinski, and Naomi Altman
Canada's Michael Smith Genome Sciences Centre, Vancouver, British Columbia, Canada.

UI MeSH Term Description Entries
D008390 Markov Chains A stochastic process such that the conditional probability distribution for a state at any future instant, given the present state, is unaffected by any additional knowledge of the past history of the system. Markov Process,Markov Chain,Chain, Markov,Chains, Markov,Markov Processes,Process, Markov,Processes, Markov
D015233 Models, Statistical Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc. Probabilistic Models,Statistical Models,Two-Parameter Models,Model, Statistical,Models, Binomial,Models, Polynomial,Statistical Model,Binomial Model,Binomial Models,Model, Binomial,Model, Polynomial,Model, Probabilistic,Model, Two-Parameter,Models, Probabilistic,Models, Two-Parameter,Polynomial Model,Polynomial Models,Probabilistic Model,Two Parameter Models,Two-Parameter Model
D019370 Observation The act of regarding attentively and studying facts and occurrences, gathering data through analyzing, measuring, and drawing conclusions, with the purpose of applying the observed information to theoretical assumptions. Observation as a scientific method in the acquisition of knowledge began in classical antiquity; in modern science and medicine its greatest application is facilitated by modern technology. Observation is one of the components of the research process.

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