Markov model


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Related to Markov model: Markov process, Hidden Markov model

Markov model

A model used in decision analysis for evaluating potential outcomes of a disease process, which are defined as specific health states, transitions among which are modelled iteratively. In standard decision tree analysis, a patient moves through states—for example, from not treated, to treated, to final outcome; in a Markov process, a patient moves between states (e.g., backwards and forward between continuous ambulatory peritoneal dialysis and haemodialysis). Some states cannot be left once entered (so-called “absorbing states”), including death.
References in periodicals archive ?
To conduct our sensitivity analysis with a Markov model, there were no data available to directly estimate death from CRPC specific to patients with high-volume disease, either from the trial or in the literature.
At this conversion rate, Markov models predicted gravel rooftops would be reduced by 54% by 2106 if gravel materials are not phased out by the roofing industry.
This section firstly re-deduces the threshold of BED algorithm, and then discusses a new algorithm that exploits the Markov model in Fig.
Continuous density hidden Markov models (CD-HMM) were used for the creation of a Lithuanian recognizer by using an open code software toolkit HTK v.3.2 (Hidden Markov Toolkit) [8].
Clements, "An application of hidden Markov models in subjectivity analysis," in Proceedings of the IEEE 7th International Conference on Application of Information and Communication Technologies (AICT '13), pp.
The initial state probability transfer matrix is calculated from the historical traffic sequence when the grey Markov model is used to predict the short-term traffic flow.
Lu, "Driver intention recognition method using continuous hidden markov model," International Journal of Computational Intelligence Systems, vol.
The direct evaluation method, in comparison, requires O([N.sup.T+k-1]) calculations where N is the number of states, T is the length of observational sequence, and k is the order of the Hidden Markov Model.
The rest of this paper proceeds as follows: "Literature" section discusses the forward-discount puzzle, "The hidden Markov model" section presents the hidden Markov model, "Analysis of Results" section analyses the initial results, "Exogenous Influences on Regime SwitchingProbabilities" section considers exogenous influences on the probability of switching from one regime to another, and "Conclusion" section concludes.
The following strategy is suitable to derive newly mixed velocity progressions from the Markov model. Based on an initially set velocity and acceleration combination, a generation is done by a query of the saved state transition in the Markov model.
Hong and Prozzi develop the pavement deterioration forecasting model based on the Bayesian approach and Markov model and use Bayesian approach to obtain probabilistic parameter distributions through a combination of existing knowledge priorly and information from the data collected [30].