Markov model

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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 ?
However, Markov models, assuming current regional rates of grassland conversion to rowcrops, along with the small proportions of protected grasslands, suggest that continued loss of grasslands will reduce grassland land cover by 2106 to levels similar to those at eastern study sites, where nighthawks occur almost exclusively in developed areas with gravel rooftops.
An n state of Markov model leads to a system of n coupled differential equations.
(2010) Hidden Markov models for financial optimization problems.
Our work is also different from previous research on the behavior of PU because we use a two-state Markov model to reduce the total cost of false alarm and missed detection instead of increasing system throughputs.
A number of automatic instruments have been developed, most frequently based on HMMs (Hidden Markov Models) [6].
du Preez, "Efficient training of high-order hidden Markov models using first-order representations," Computer Speech and Language, vol.
Clements, "Sentence-level subjectivity detection using neuro-fuzzy and hidden markov models," in Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis in (NAACLHLT '13), pp.
Fridman, "Distinguishing patterns in drivers' visual attention allocation using Hidden Markov Models," Transportation Research Part F: Traffic Psychology and Behaviour, vol.
This paper uses a two-regime model to understand more about crash risk by assessing uncovered interest parity (UIP) deviations in a range of CEE countries and by using a hidden Markov model (HMM) to divide the deviations into two categories: those where the high-yield currency appreciates against the lower interest rate unit (adding a capital gain to the funding premium) and those where the high-yield currency falls much more than would be anticipated by UIP.
Whereas Markov regime switching models can be considered as generalization of Hidden Markov models, the method proposed in this paper with two state regimes would compose a more naturalistic model.
Probabilistic models include Markov models, survival analysis, and Bayesian approach.