Package jline.lang.processes

Class MarkedMMPP

  • All Implemented Interfaces:
    java.io.Serializable , jline.lang.Copyable 
    
public class MarkedMMPP
extends Marked implements Serializable

    A Marked Markov-Modulated Poisson Process (M3PP)

    • Nested Class Summary

      Nested Classes 
      Modifier and Type Class Description

    • Enum Constant Summary

      Enum Constants 
      Enum Constant Description

    • Method Summary

      Modifier and Type Method Description
      double evalCDF(double t) Evaluates the cumulative distribution function (CDF) at time t.
      double evalLST(double s) Evaluates the Laplace-Stieltjes Transform (LST) at parameter s.
      MatrixCell getProcess() Returns the process representation as a MatrixCell.
      double getRate() Gets the rate of this distribution (inverse of mean).
      double getSCV() Gets the squared coefficient of variation (SCV) of this distribution.
      double getSkewness() Gets the skewness of this distribution.
      double getVar() Gets the variance of this distribution.
      void normalize() Normalizes the MarkedMMPP so that D0+sum(D_i) rows form a proper infinitesimal generator.
      Array<double> sample(int n) Generates random samples from this distribution using default random generator.
      Array<double> sample(int n, Random random) Generates samples from the MarkedMMPP using the specified random generator.
      MarkedMMPP toTimeReversed()

      • Methods inherited from class jline.lang.processes.Marked

        D, D, getD1k

      • Methods inherited from class jline.lang.processes.MarkovModulated

        evalACFT, getACFDecay

      • Methods inherited from class jline.lang.processes.Markovian

        acf, embedded, embeddedProb, evalCDF, evalMeanT, evalVarT, getACF, getEmbedded, getEmbeddedProb, getIDC, getIDI, getInitProb, getMean, getMoments, getMu, getNumberOfPhases, getPhi, getSubgenerator, getVariance, idc, idi, initProb, mean, moments, mu, numPhases, numberOfPhases, phi, process, rate, scv, setMean, setProcess, setRate, skewness, subgenerator, var, variance

      • Methods inherited from class jline.lang.processes.Distribution

        evalProbInterval, getName, getNumParams, getParam, getSupport, isContinuous, isDisabled, isDiscrete, isImmediate, isMarkovian, name, numParams, param, setNumParams, setParam, support

      • Methods inherited from class jline.lang.Copyable

        copy

      • Methods inherited from class java.lang.Object

        clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

    • Constructor Detail

      • MarkedMMPP

        MarkedMMPP()

    • Method Detail

      • evalCDF

         double evalCDF(double t)

        Evaluates the cumulative distribution function (CDF) at time t.

        Parameters:
        t - the time value
        Returns:

        the CDF value at time t

      • evalLST

         double evalLST(double s)

        Evaluates the Laplace-Stieltjes Transform (LST) at parameter s.

        Parameters:
        s - the transform parameter
        Returns:

        the LST value at parameter s

      • getProcess

         MatrixCell getProcess()

        Returns the process representation as a MatrixCell.

        Returns:

        the process matrices

      • getRate

         double getRate()

        Gets the rate of this distribution (inverse of mean).

        Returns:

        the rate value (1/mean)

      • getSCV

         double getSCV()

        Gets the squared coefficient of variation (SCV) of this distribution. SCV = Var(X) / E[X]^2.

        Returns:

        the squared coefficient of variation

      • getSkewness

         double getSkewness()

        Gets the skewness of this distribution. Skewness measures the asymmetry of the probability distribution.

        Returns:

        the skewness value

      • getVar

         double getVar()

        Gets the variance of this distribution. Computed as SCV * mean^2.

        Returns:

        the variance

      • normalize

         void normalize()

        Normalizes the MarkedMMPP so that D0+sum(D_i) rows form a proper infinitesimal generator. For MMPP, all D_i matrices should be diagonal with non-negative elements.

      • sample

         Array<double> sample(int n)

        Generates random samples from this distribution using default random generator.

        Parameters:
        n - the number of samples to generate
        Returns:

        array of random samples

      • sample

         Array<double> sample(int n, Random random)

        Generates samples from the MarkedMMPP using the specified random generator.

        Parameters:
        n - the number of samples to generate
        random - the random number generator to use
        Returns:

        array of inter-arrival times