com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde

Class Ft

java.lang.Object

com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.Ft

All Implemented Interfaces:

DeepCopyable, java.io.Serializable

Direct Known Subclasses:

FtWt

public class Ft
extends java.lang.Object
implements DeepCopyable, java.io.Serializable

This represents the concept 'Filtration', the information available at time t.

The information may include (subject to implementation), for example,

• time
• value of the stochastic process
• values of the driving Brownian motion(s)

See Also:

"Fima C. Klebaner. Introduction to Stochastic Calculus with Applications. 2nd ed. pp.23. Imperial College Press. 2006.", Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
protected double	dt the time differential
protected Vector	Xt the value of the stochastic process at time t
protected Vector	Zt a sampling from the Gaussian distribution

Constructor Summary

Constructors

Constructor and Description
Ft() Construct an empty filtration (no information).
Ft(Ft that) Copy constructor.

Method Summary

Modifier and Type	Method and Description
Ft	deepCopy() The implementation returns an instance created from this by the copy constructor of the class, or just this if the instance itself is immutable.
int	dim() Get the dimension of the process.
double	dt() Get the current time differential.
Vector	dWt() Get the increment of the driving Brownian motion during the time differential.
int	nB() Get the number of independent Brownian motions.
void	setDt(double dt) Set the current time differential.
void	setXt(Vector Xt) Set the current value of the stochastic process.
void	setZt(Vector Zt) Set the value of the Gaussian distribution innovation.
Vector	Xt() Get the current value of the stochastic process.
Vector	Zt() Get the current value of the Gaussian distribution innovation.

Methods inherited from class java.lang.Object

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

Field Detail

dt

protected double dt

the time differential

Xt

protected Vector Xt

the value of the stochastic process at time t

Zt

protected Vector Zt

a sampling from the Gaussian distribution

Constructor Detail

Ft

public Ft()

Construct an empty filtration (no information).

Ft

public Ft(Ft that)

Copy constructor.

Parameters:

that - another Ft

Method Detail

deepCopy

public Ft deepCopy()

Description copied from interface: DeepCopyable

The implementation returns an instance created from this by the copy constructor of the class, or just this if the instance itself is immutable.

Specified by:

deepCopy in interface DeepCopyable

Returns:

an independent (deep) copy of the instance

dim

public int dim()

Get the dimension of the process.

Returns:

the dimension of the process

nB

public int nB()

Get the number of independent Brownian motions.

Returns:

the number of independent Brownian motions

setDt

public void setDt(double dt)

Set the current time differential.

Parameters:

dt - the time differential

dt

public double dt()

Get the current time differential.

Returns:

the time differential

setXt

public void setXt(Vector Xt)

Set the current value of the stochastic process.

Parameters:

Xt - the current value of the stochastic process

Xt

public Vector Xt()

Get the current value of the stochastic process.

Returns:

the current value of the stochastic process

setZt

public void setZt(Vector Zt)

Set the value of the Gaussian distribution innovation.

Parameters:

Zt - the Gaussian distribution innovation

Zt

public Vector Zt()

Get the current value of the Gaussian distribution innovation.

Returns:

the current Gaussian distribution innovation

dWt

public Vector dWt()

Get the increment of the driving Brownian motion during the time differential.

This is the product of the Gaussian distribution innovation and the square root of the time differential.

Returns:

the increment of the driving Brownian motion during the time differential