org.ojalgo.random.process

Class PoissonProcess

java.lang.Object

org.ojalgo.random.process.PoissonProcess

All Implemented Interfaces:

RandomProcess<Poisson>

public final class PoissonProcess
extends Object

A Poisson process is a stochastic process which counts the number of events in a given time interval. The time between each pair of consecutive events has an exponential distribution with parameter λ and each of these inter-arrival times is assumed to be independent of other inter-arrival times. The process is a good model of radioactive decay, telephone calls and requests for a particular document on a web server, among many other phenomena.

Author:

apete

Nested Class Summary

Nested classes/interfaces inherited from interface org.ojalgo.random.process.RandomProcess

RandomProcess.SimulationResults

Constructor Summary

Constructors

Modifier	Constructor and Description
protected	PoissonProcess(double rate)

Method Summary

Modifier and Type	Method and Description
boolean	addObservation(Double x, double y)
Poisson	getDistribution(double evaluationPoint)
double	getExpected() Equivalent to calling RandomProcess.getDistribution(double) with argumant 1.0, and then Distribution.getExpected().
double	getLowerConfidenceQuantile(double confidence) The same thing can be achieved by first calling RandomProcess.getDistribution(double) with argumant 1.0, and then ContinuousDistribution.getQuantile(double) (but with different input argument).
protected double	getNormalisedRandomIncrement()
double	getStandardDeviation() Equivalent to calling RandomProcess.getDistribution(double) with argumant 1.0, and then Distribution.getStandardDeviation().
Exponential	getTimeBetweenConsecutiveEvents()
double	getUpperConfidenceQuantile(double confidence) The same thing can be achieved by first calling RandomProcess.getDistribution(double) with argumant 1.0, and then ContinuousDistribution.getQuantile(double) (but with different input argument).
double	getValue()
double	getVariance() Equivalent to calling RandomProcess.getDistribution(double) with argumant 1.0, and then Distribution.getVariance().
protected void	setObservations(Collection<? extends ComparableToDouble<Double>> c)
void	setValue(double newValue)
RandomProcess.SimulationResults	simulate(int numberOfRealisations, int numberOfSteps, double stepSize)
protected double	step(double currentValue, double stepSize, double normalisedRandomIncrement)

Methods inherited from class java.lang.Object

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

Constructor Detail

PoissonProcess

protected PoissonProcess(double rate)

Method Detail

getDistribution

public Poisson getDistribution(double evaluationPoint)

Parameters:

evaluationPoint - How far into the future?

Returns:

The distribution for the process value at that future time.

getTimeBetweenConsecutiveEvents

public Exponential getTimeBetweenConsecutiveEvents()

getNormalisedRandomIncrement

protected double getNormalisedRandomIncrement()

step

protected double step(double currentValue,
                      double stepSize,
                      double normalisedRandomIncrement)

addObservation

public final boolean addObservation(Double x,
                                    double y)

getExpected

public final double getExpected()

Equivalent to calling RandomProcess.getDistribution(double) with argumant 1.0, and then Distribution.getExpected().

getLowerConfidenceQuantile

public final double getLowerConfidenceQuantile(double confidence)

The same thing can be achieved by first calling RandomProcess.getDistribution(double) with argumant 1.0, and then ContinuousDistribution.getQuantile(double) (but with different input argument).

getStandardDeviation

public final double getStandardDeviation()

Equivalent to calling RandomProcess.getDistribution(double) with argumant 1.0, and then Distribution.getStandardDeviation().

getUpperConfidenceQuantile

public final double getUpperConfidenceQuantile(double confidence)

The same thing can be achieved by first calling RandomProcess.getDistribution(double) with argumant 1.0, and then ContinuousDistribution.getQuantile(double) (but with different input argument).

getValue

public final double getValue()

getVariance

public final double getVariance()

Equivalent to calling RandomProcess.getDistribution(double) with argumant 1.0, and then Distribution.getVariance().

setValue

public final void setValue(double newValue)

simulate

public final RandomProcess.SimulationResults simulate(int numberOfRealisations,
                                                      int numberOfSteps,
                                                      double stepSize)

Specified by:

simulate in interface RandomProcess<D extends Distribution>

Returns:

An array of sample sets. The array has aNumberOfSteps elements, and each sample set has aNumberOfRealisations samples.

setObservations

protected final void setObservations(Collection<? extends ComparableToDouble<Double>> c)