Markov model

(redirected from Markov chains)
Also found in: Dictionary, Thesaurus, 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 ?
Four subtasks are included: 1) A building Markov deterioration model is established based on building condition assessment through which the practical building service lifetime is estimated; 2) The ambient temperature variation is simulated using Markov Chain by examining the transition of the degree day level; 3) The annual energy consumption distribution for each possible combination of building condition and degree-day level is projected through neural network technique based on a measured dataset; 4) The transition between the possible energy consumption state (i.
2006, 2010, 2011, 2012b, c; Cimanis, Paramonov 2012), which are devoted to the connection of tensile strength distribution parameters and parameters of fatigue curve, S-N, for unidirectional fibre composite using the model, based on the Markov chain (MCh) theory.
Aurbacher J, Dabbert S (2011) Generating crop sequences in land-use models using maximum entropy and Markov chains.
As is the case for previously analysed Hopf-power Markov chains, all eigenvalues of this descent set chain are powers of the Hopf-power exponent a.
SCHUTTE, Identification of almost invariant aggregates in reversible nearly uncoupled Markov chains, Linear Algebra Appl.
As a stochastic approach, Markov chain is applied to provide valuable information about the state of the pavement in the future.
In a previous study (Liu et al, 2010) a Markov chain approach was developed to determine the transitions among payment states of a mortgage loan.
Therefore, a terminal will be represented as homogenous Markov chain with the following matrix of transitional probabilities:
i) ascertain the predictive ability of Markov Chains in stock price analysis
In this paper, we use a finite Markov chain approach to provide an indexing procedure by which one can monitor the health status of a mortgage loan over time.
This paper focuses on time-homogenous Markov chains, in which