LimDist: Tool to computate the limiting distribution for a Continuous...
In modesto: Modeling and Analysis of Stochastic Systems
LimDist R Documentation
Tool to computate the limiting distribution for a Continuous Time Markov Chain, CTMC.
Description
LimDist is used to obtain the limiting distribution of a homogeneous continuous time Markov chain.
Usage
LimDist(X, rate, epsilon = 0.01, iter)
Arguments
X
matrix, represents a rate matrix of a CTMC or the transition probability matrix of the DTMC associated to the CTMC.
rate
boolean, if rate is equal to TRUE then the argument X represents the rate matrix of the CTMC. If rate is equal to FALSE then the argument X represents the probability transition matrix of the CTMC.
epsilon
numeric, represents the error of approximation.
iter
integer, represents the maximum of iterations.
Author(s)
Carlos Alberto Cardozo Delgado <cardozorpackages@gmail.com>.
References
Ross, S, Introduction to Probability Models, Eleven Edition. Academic Press, 2014.
Kulkarni V, Introduction to modeling and analysis of stochastic systems. Second Edition. Springer-Verlag, 2011.
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| LimDist | R Documentation |
Tool to computate the limiting distribution for a Continuous Time Markov Chain, CTMC.
Description
LimDist is used to obtain the limiting distribution of a homogeneous continuous time Markov chain.
Usage
LimDist(X, rate, epsilon = 0.01, iter)
Arguments
X |
matrix, represents a rate matrix of a CTMC or the transition probability matrix of the DTMC associated to the CTMC. |
rate |
boolean, if rate is equal to TRUE then the argument X represents the rate matrix of the CTMC. If rate is equal to FALSE then the argument X represents the probability transition matrix of the CTMC. |
epsilon |
numeric, represents the error of approximation. |
iter |
integer, represents the maximum of iterations. |
Author(s)
Carlos Alberto Cardozo Delgado <cardozorpackages@gmail.com>.
References
Ross, S, Introduction to Probability Models, Eleven Edition. Academic Press, 2014.
Kulkarni V, Introduction to modeling and analysis of stochastic systems. Second Edition. Springer-Verlag, 2011.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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