public class WienerProcess extends DistNormal
dt << tau
. A Wiener process is a process that results
in random values that follow a Normal distribution with parameters mu and sigma. However, there is a time progression with
correlation tau
, such that the process has a tendency to stay close to the previous value. The correlation time
tau
is a measure for this tendency. Given sufficient time (and dt << tau
) the overall probability remains
equal to the Normal distribution.
The Wiener process is typically used for measurement or perception errors in cases where the error of two consecutive time steps is unlikely to deviate much.
Treiber, M., A. Kesting, D. Helbing (2006) "Delays, Inaccuracies and Anticipation in Microscopic Traffic Models", Physica A – Statistical Mechanics and its Applications, Vol. 360, Issue 1, pp. 71-88.
Copyright (c) 2013-2019 Delft University of Technology, PO Box 5, 2600 AA, Delft, the Netherlands. All rights reserved.
BSD-style license. See OpenTrafficSim License.
CUMULATIVE_NORMAL_PROBABILITIES, haveNextNextGaussian, mu, sigma
Constructor and Description |
---|
WienerProcess(StreamInterface stream,
double mu,
double sigma,
Duration tau,
OTSSimulatorInterface simulator) |
Modifier and Type | Method and Description |
---|---|
double |
draw() |
getCumulativeProbability, getInverseCumulativeProbability, nextGaussian, probDensity, toString
public WienerProcess(StreamInterface stream, double mu, double sigma, Duration tau, OTSSimulatorInterface simulator)
stream
- StreamInterface; random number streammu
- double; meansigma
- double; standard deviationtau
- Duration; correlation timesimulator
- OTSSimulatorInterface; simulatorpublic double draw()
draw
in class DistNormal
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