# 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.
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Signorelli, The R Package "Markovchain": Easily Handling Discrete Markov Chains in R, 2014.
We model the [Ca.sup.2+] channel by using the 3-state Markov chain of Figure 1(a), where C corresponds to the closed state, O to the open state, and B to the inactivated (blocked) state of the calcium channel .
Markov chains are required to be ergodic; therefore it can be ensured that the leader has directed paths to all followers in [G.sup.u].
COMPUTER SIMULATIONS OF MARKOV CHAINS AND MONTE CARLO METHODS
The probability [p.sub.n] that the discrete-time Markov chain defined in Section 2.1, starting from n, will hit N before 1 is given by (40) if a [not equal to] 1/2.
Nunez-Queija, "Perturbation analysis for denumerable Markov chains with application to queueing models," Advances in Applied Probability, vol.
In this section, we present the combinatorial characterization of output functions of Markov chains which are asymptotically independent and of Markov chains with output functions with a singular variance-covariance matrix.
Markov Chains are based on two considerations, the first is the supposition that lithology at any point n depends upon the lithology of the proceeding point n-1.
For the single latent dual redundant system we can ignore the discrete repair assumption (repair only on ground) and model the system with a continuous time Markov chain model.
In this section, we provide a discrete-time Markov chain model for the analysis of the CSMA/CA-based IEEE 802.15.6 MAC.
(4.) Walsh, B., "Markov Chain Monte Carlo and Gibbs Sampling," Lecture notes for EEB 581, April 2004.
Let {[N.sup.(1).sub.1], i [member of] the N}, {[N.sup.(2).sub.i], i [member of] N} be the corresponding embedded Markov chains as well as their stationary distributions {[[pi].sup.(1).sub.n]}, {[[pi].sup.(2).sub.n]}, respectively.

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