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.
A New Weighting Scheme in Weighted Markov Model for Predicting the Probability of Drought Episodes
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 [11].
Explicit Theoretical Analysis of How the Rate of Exocytosis Depends on Local Control by [Ca.sup.2+] Channels
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].
Distributed Tracking Control for Discrete-Time Multiagent Systems with Novel Markovian Switching Topologies
COMPUTER SIMULATIONS OF MARKOV CHAINS AND MONTE CARLO METHODS
APPLICATIONS AND COMPUTER SIMULATIONS OF MARKOV CHAINS
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.
First Passage Time of a Markov Chain That Converges to Bessel Process
Nunez-Queija, "Perturbation analysis for denumerable Markov chains with application to queueing models," Advances in Applied Probability, vol.
A Taylor Series Approach for Service-Coupled Queueing Systems with Intermediate Load
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.
Variance and covariance of several simultaneous outputs of a Markov chain
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.
Lithofacies cyclicity determination in the guaduas formation (Colombia) using Markov chains
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.
Average probability calculation methods for system safety analysis
In this section, we provide a discrete-time Markov chain model for the analysis of the CSMA/CA-based IEEE 802.15.6 MAC.
Modeling and performance analysis of MAC protocol for WBAN with finite buffer
(4.) Walsh, B., "Markov Chain Monte Carlo and Gibbs Sampling," Lecture notes for EEB 581, April 2004.
Applying Bayes' Theorem to clinical trials
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.
Insensitive bounds for the stationary distribution of a single server retrial queue with server subject to active breakdowns

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