Class MultivariateEulerSDE
- java.lang.Object
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- dev.nm.stat.stochasticprocess.multivariate.sde.discrete.MultivariateEulerSDE
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- All Implemented Interfaces:
MultivariateDiscreteSDE
public class MultivariateEulerSDE extends Object implements MultivariateDiscreteSDE
The Euler scheme is the first order approximation of an SDE. \[ dX_t = \mu * dt + \sigma * \sqrt{dt} * Z_t \]- See Also:
- Wikipedia: Euler-Maruyama method
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Constructor Summary
Constructors Constructor Description MultivariateEulerSDE(MultivariateSDE sde)Discretize a multivariate, continuous-time SDE using the Euler scheme.
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description VectordXt(MultivariateFt ft)This is the SDE specification of a stochastic process.MultivariateFtgetNewFt()Get an empty filtration of the process.intnB()Get the number of independent driving Brownian motions.
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Constructor Detail
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MultivariateEulerSDE
public MultivariateEulerSDE(MultivariateSDE sde)
Discretize a multivariate, continuous-time SDE using the Euler scheme.- Parameters:
sde- a continuous-time SDE
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Method Detail
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dXt
public Vector dXt(MultivariateFt ft)
This is the SDE specification of a stochastic process. \(dX_t = \mu * dt + \sigma * \sqrt{dt} * Z_t\)- Specified by:
dXtin interfaceMultivariateDiscreteSDE- Parameters:
ft- a filtration- Returns:
- the increment of the process in
dt
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nB
public int nB()
Description copied from interface:MultivariateDiscreteSDEGet the number of independent driving Brownian motions.- Specified by:
nBin interfaceMultivariateDiscreteSDE- Returns:
- the number of independent driving Brownian motions
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getNewFt
public MultivariateFt getNewFt()
Description copied from interface:MultivariateDiscreteSDEGet an empty filtration of the process.- Specified by:
getNewFtin interfaceMultivariateDiscreteSDE- Returns:
- an empty filtration
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