All Implemented Interfaces:

java.io.Serializable , jline.lang.Copyable
```
public class MMPP2
extends MarkovModulated implements Serializable
                    
```
A Markovian-modulated Poisson Process with 2 states

Nested Class Summary

Nested Classes
Modifier and Type	Class	Description

Field Summary

Fields
Modifier and Type	Field	Description
`protected MatrixCell`	`process`
`protected int`	`nPhases`
`protected double`	`mean`
`protected boolean`	`immediate`
`protected String`	`name`
`protected int`	`numParam`
`protected Pair<Double, Double>`	`support`
`protected List<NamedParam>`	`params`

Constructor Summary

Constructors
Constructor	Description
`MMPP2(double lambda0, double lambda1, double sigma0, double sigma1)`

Enum Constant Summary

Enum Constants
Enum Constant	Description

Method Summary

Modifier and Type	Method	Description
`static MMPP2`	`fitCentralAndACFDecay(double mean, double var, double skew, double g2)`
`static MMPP2`	`fitCentralAndACFLag1(double mean, double var, double skew, double acf1)`
`static MMPP2`	`fitCentralAndIDC(double mean, double var, double skew, double idc)`
`static MMPP2`	`fitRawMomentsAndACFDecay(double m1, double m2, double m3, double gamma2)`
`static MMPP2`	`fitRawMomentsAndACFLag1(double m1, double m2, double m3, double rho1)`
`static MMPP2`	`fitRawMomentsAndIDC(double m1, double m2, double m3, double idc)`
`static MMPP2`	`rand()`
`Matrix`	`D(int i)`	Gets the i-th matrix of the Markovian arrival process representation.
`Matrix`	`evalACFT(Array<int> lags, double timescale)`
`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.
`double`	`evalMeanT(double t)`	Evaluates the mean count at time t.
`double`	`getACFDecay(Matrix lags)`
`Matrix`	`getEmbedded()`	Gets the embedded Markov chain transition matrix.
`Matrix`	`getEmbeddedProb()`	Gets the stationary probability vector of the embedded Markov chain.
`double`	`getIDC()`	Gets the index of dispersion for counts (IDC).
`double`	`getIDI()`	Gets the index of dispersion for intervals (IDI).
`double`	`getMean()`	Gets the mean of this Markovian distribution.
`long`	`getNumberOfPhases()`	Gets the number of phases in this Markovian distribution.
`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 MMPP2 so that D0+D1 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 random samples from this distribution using the specified random generator.
`MAP`	`toTimeReversed()`
`double`	`evalPDF(double t)`	Evaluates the probability density function (PDF) at time t.
`double`	`evalVarT(double t)`	Evaluates the variance at time t.
`String`	`toString()`	Returns a string representation of this MMPP2 process.
`boolean`	`equals(Object obj)`	Checks if this MMPP2 is equal to another object.
`int`	`hashCode()`	Returns the hash code for this MMPP2 process.

Methods inherited from class jline.lang.processes.MarkovModulated
getACFDecay
Methods inherited from class jline.lang.processes.Markovian
acf, embedded, embeddedProb, evalCDF, getACF, getInitProb, getMoments, getMu, getPhi, getProcess, 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
  - MMPP2
```
MMPP2(double lambda0, double lambda1, double sigma0, double sigma1)
```
- Method Detail
  - fitCentralAndACFDecay
```
 static MMPP2 fitCentralAndACFDecay(double mean, double var, double skew, double g2)
```
  - fitCentralAndACFLag1
```
 static MMPP2 fitCentralAndACFLag1(double mean, double var, double skew, double acf1)
```
  - fitCentralAndIDC
```
 static MMPP2 fitCentralAndIDC(double mean, double var, double skew, double idc)
```
  - fitRawMomentsAndACFDecay
```
 static MMPP2 fitRawMomentsAndACFDecay(double m1, double m2, double m3, double gamma2)
```
  - fitRawMomentsAndACFLag1
```
 static MMPP2 fitRawMomentsAndACFLag1(double m1, double m2, double m3, double rho1)
```
  - fitRawMomentsAndIDC
```
 static MMPP2 fitRawMomentsAndIDC(double m1, double m2, double m3, double idc)
```
  - rand
```
 static MMPP2 rand()
```
  - D
```
 Matrix D(int i)
```
    Gets the i-th matrix of the Markovian arrival process representation.
    
    Parameters:
    
    i - the matrix index (0 for D0, 1 for D1, etc.
    
    Returns:
    
    the matrix at index i
  - evalACFT
```
 Matrix evalACFT(Array<int> lags, double timescale)
```
  - evalCDF
```
 double evalCDF(double t)
```
    Evaluates the cumulative distribution function (CDF) at time t. For MMPP2, this represents the probability that an inter-arrival time is less than or equal to 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. For MMPP2, LST(s) = alpha * (-T + s*I)^(-1) * t
    
    Parameters:
    
    s - the transform parameter
    
    Returns:
    
    the LST value at parameter s
  - evalMeanT
```
 double evalMeanT(double t)
```
    Evaluates the mean count at time t.
    
    Parameters:
    
    t - the time point
    
    Returns:
    
    the mean count at time t
  - getACFDecay
```
 double getACFDecay(Matrix lags)
```
  - getEmbedded
```
 Matrix getEmbedded()
```
    Gets the embedded Markov chain transition matrix.
    
    Returns:
    
    the embedded chain matrix
  - getEmbeddedProb
```
 Matrix getEmbeddedProb()
```
    Gets the stationary probability vector of the embedded Markov chain.
    
    Returns:
    
    the embedded chain stationary probabilities
  - getIDC
```
 double getIDC()
```
    Gets the index of dispersion for counts (IDC).
    
    Returns:
    
    the IDC value
  - getIDI
```
 double getIDI()
```
    Gets the index of dispersion for intervals (IDI). For renewal processes, IDI = IDC.
    
    Returns:
    
    the IDI value
  - getMean
```
 double getMean()
```
    Gets the mean of this Markovian distribution.
    
    Returns:
    
    the mean value, or NaN if the process contains NaN values
  - getNumberOfPhases
```
 long getNumberOfPhases()
```
    Gets the number of phases in this Markovian distribution.
    
    Returns:
    
    the number of phases
  - 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 MMPP2 so that D0+D1 rows form a proper infinitesimal generator. Each row sum of (D0+D1) should equal zero for a valid generator.
  - 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 random samples from this distribution using the specified random generator.
    
    Parameters:
    
    n - the number of samples to generate
    
    random - the random number generator to use
    
    Returns:
    
    array of random samples
  - toTimeReversed
```
 MAP toTimeReversed()
```
  - evalPDF
```
 double evalPDF(double t)
```
    Evaluates the probability density function (PDF) at time t. For MMPP2, this is the derivative of the CDF.
    
    Parameters:
    
    t - the time value
    
    Returns:
    
    the PDF value at time t
  - evalVarT
```
 double evalVarT(double t)
```
    Evaluates the variance at time t. For MMPP2, this provides the variance of the counting process at time t.
    
    Parameters:
    
    t - the time value
    
    Returns:
    
    the variance at time t
  - toString
```
 String toString()
```
    Returns a string representation of this MMPP2 process.
    
    Returns:
    
    string representation showing parameters
  - equals
```
 boolean equals(Object obj)
```
    Checks if this MMPP2 is equal to another object.
    
    Parameters:
    
    obj - the object to compare with
    
    Returns:
    
    true if equal, false otherwise
  - hashCode
```
 int hashCode()
```
    Returns the hash code for this MMPP2 process.
    
    Returns:
    
    hash code based on parameters

Class MMPP2

Nested Class Summary

Field Summary

Constructor Summary

Enum Constant Summary

Method Summary

Methods inherited from class jline.lang.processes.MarkovModulated

Methods inherited from class jline.lang.processes.Markovian

Methods inherited from class jline.lang.processes.Distribution

Methods inherited from class jline.lang.Copyable

Methods inherited from class java.lang.Object

Constructor Detail

MMPP2

Method Detail

fitCentralAndACFDecay

fitCentralAndACFLag1

fitCentralAndIDC

fitRawMomentsAndACFDecay

fitRawMomentsAndACFLag1

fitRawMomentsAndIDC

rand

D

evalACFT

evalCDF

evalLST

evalMeanT

getACFDecay

getEmbedded

getEmbeddedProb

getIDC

getIDI

getMean

getNumberOfPhases

getRate

getSCV

getSkewness

getVar

normalize

sample

sample

toTimeReversed

evalPDF

evalVarT

toString

equals

hashCode