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.

Markov chain, Markov model

a mathematical model that makes it possible to study complex systems by establishing a state of the system and then effecting a transition to a new state, such a transition being dependent only on the values of the current state, and not dependent on the previous history of the system up to that point.
References in periodicals archive ?
A Markov model describes the probability of moving from one set of states to another.
To verify our Markov model and theoretical analysis of CQM protocol, we compared theoretical analysis results with simulation results obtained by Qualnet simulator.
2013) compared different techniques of action recognition using Hidden Markov Models (HMM).
One team for which these advances did not have the highest threshold using the Markov model is the Kansas City Royals.
Wang, "Driving intention recognition and behaviour prediction based on a double-layer hidden Markov model," Journal of Zhejiang University: Science C, vol.
In some cases where the latent events are localized to small sub-trees of a larger FT, the sub-tree can be modeled as a Markov model and its [P.
One of the major strengths of the Markov model is the way it simply and intuitively handles both cost and outcomes of different treatment processes.
Later progress in error modeling introduced new mathematical concepts and model classes, often referred to as pure models: semi-Markov models, Hidden Markov Models (HMMs), empirical approaches including algorithmic models, chaos models, Deterministic Process Based Generative Models (DPBGM) and Stochastic Context-Free Grammar models (SCFG).
In this paper, we implemented a machine-learning based processes, the Hidden Markov Model.
2- Pi represents the probability to traverse from state to state in Markov Model
Time requirements to perform the uncertainty analysis by simulation approach and Markov model is shown in Table III.
In tins paper, we implemented a machine-learning based processes, the Hidden Markov Model.