Package jline.lang.processes

Class MMDP2

  • All Implemented Interfaces:
    java.io.Serializable , jline.lang.Copyable 
    
public class MMDP2
extends MMDP implements Serializable

    A 2-state Markov-Modulated Deterministic Process. Specialized MMDP with exactly 2 phases, using a convenient parameterization analogous to MMPP2. Parameterization:

    • r0, r1: Deterministic rates in states 0 and 1
    • sigma0: Transition rate from state 0 to state 1
    • sigma1: Transition rate from state 1 to state 0
    The generator matrix is: Q = [-sigma0, sigma0; sigma1, -sigma1] The rate matrix is: R = diag([r0, r1])

    • Nested Class Summary

      Nested Classes 
      Modifier and Type Class Description

    • Constructor Summary

      Constructors 
      Constructor Description
      MMDP2(double r0, double r1, double sigma0, double sigma1) Creates a 2-state Markov-Modulated Deterministic Process.

    • Enum Constant Summary

      Enum Constants 
      Enum Constant Description

    • Method Summary

      Modifier and Type Method Description
      Matrix Q() Returns the generator matrix Q (closed-form for 2 states).
      Matrix R() Returns the rate matrix R (diagonal, closed-form for 2 states).
      Matrix r() Returns the rate vector (diagonal of R).
      long getNumberOfPhases() Gets the number of phases in this Markovian distribution.
      double getMeanRate() Computes the stationary mean rate (closed-form).
      double getSCV() Computes the squared coefficient of variation (closed-form).
      String toString()

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

        fromMAP, fromMMPP2, getMean, getProcess, getRate, isFeasible

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

        D, acf, embedded, embeddedProb, evalCDF, evalCDF, evalLST, evalMeanT, evalVarT, getACF, getEmbedded, getEmbeddedProb, getIDC, getIDI, getInitProb, getMoments, getMu, getPhi, getSkewness, getSubgenerator, getVar, getVariance, idc, idi, initProb, mean, moments, mu, numPhases, numberOfPhases, phi, process, rate, sample, sample, 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

      • MMDP2

        MMDP2(double r0, double r1, double sigma0, double sigma1)
        Creates a 2-state Markov-Modulated Deterministic Process.
        Parameters:
        r0 - deterministic rate in state 0
        r1 - deterministic rate in state 1
        sigma0 - transition rate from state 0 to state 1
        sigma1 - transition rate from state 1 to state 0

    • Method Detail

      • Q

         Matrix Q()

        Returns the generator matrix Q (closed-form for 2 states).

        Returns:

        2x2 generator matrix

      • R

         Matrix R()

        Returns the rate matrix R (diagonal, closed-form for 2 states).

        Returns:

        2x2 diagonal rate matrix

      • r

         Matrix r()

        Returns the rate vector (diagonal of R).

        Returns:

        2-vector of rates [r0; r1]

      • getNumberOfPhases

         long getNumberOfPhases()

        Gets the number of phases in this Markovian distribution.

        Returns:

        the number of phases

      • getMeanRate

         double getMeanRate()

        Computes the stationary mean rate (closed-form). For a 2-state MMDP, the mean rate has the closed form: E[r] = (r0*sigma1 + r1*sigma0) / (sigma0 + sigma1)

        Returns:

        stationary mean deterministic rate

      • getSCV

         double getSCV()

        Computes the squared coefficient of variation (closed-form). For a 2-state MMDP, the SCV has a closed form based on the variance of rates over the stationary distribution.

        Returns:

        squared coefficient of variation