Markov model

(redirected from Markov models)
Also found in: Encyclopedia.

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 ?
2010: depmixS4: An R package for hidden Markov models.
The Markov model is obtained by combining channel hopping stratege of CQM protocol and IEEE 802.
Hidden Markov Model (HMM) has been a very popular technique for modeling and classifying dynamic gestures (Tanand Guo, 2012).
Since PGMs are very expressive and can provide many effective learning and inferring algorithms, many researchers applied different sorts of PGMs to model the planning or strategy, such as conditional random fields (CRFs) [15], Markov logic networks (MLNs) [16], dynamic Bayesian networks (DBNs) [17], hidden Markov models (HMMs) [18], Markov decision processes (MDPs) [19], and other extensions [20].
For larger systems the methods available are to use numerical solution of Markov models and applying Eq.
A Markov model was used to assess the cost and outcome of SNM, BonT-A and OMT as comparators in patients with refractory OAB failing on conservative management and first-line OMT (Fig.
A generalized Markov model construction for partially dependent events in a form of cascade Gilbert model is presented in [6] and later extended to a cascaded combination of Gilbert and Elliot models in [7].
This paper proposes a new approach by combining a Hidden Markov Model (HMM) based FDD method and a data fusion method.
To predict or anticipate a future situation, learning techniques as Markov Chains, Hidden Markov Models, Bayesian Networks, Time Series or Neural Networks are obvious candidates.
One of the advantages of Markov model is that it allows generating all possible system states and calculating steady states probabilities of the rarest failure scenarios.
Issues in Using Hidden Markov Models for Speech Recognition.
For example, four separate Markov models can be used to represent the failure of a component.