Module ojalgo

Package 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	Description
protected	PoissonProcess(double rate)

Method Summary

Modifier and Type	Method	Description
Poisson	getDistribution(double evaluationPoint)
final double	getExpected()	Equivalent to calling RandomProcess.getDistribution(double) with argumant 1.0, and then Distribution.getExpected().
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).
final double	getStandardDeviation()	Equivalent to calling RandomProcess.getDistribution(double) with argumant 1.0, and then Distribution.getStandardDeviation().
Exponential	getTimeBetweenConsecutiveEvents()
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).
final double	getVariance()	Equivalent to calling RandomProcess.getDistribution(double) with argumant 1.0, and then Distribution.getVariance().
RandomProcess.SimulationResults	simulate(int numberOfRealisations, int numberOfSteps, double stepSize)	Returns an collection of sample sets.

Methods inherited from class java.lang.Object

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

Constructor Details

PoissonProcess

protected PoissonProcess(double rate)

Method Details

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()

simulate

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

Description copied from interface: RandomProcess

Returns an collection of sample sets. The array has numberOfSteps elements, and each sample set has numberOfRealisations samples.

Returns:

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

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).

getVariance

public final double getVariance()

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