Uses of Interface
dev.nm.analysis.function.rn2r1.BivariateRealFunction
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Uses of BivariateRealFunction in dev.nm.algebra.linear.matrix.doubles.operation
Methods in dev.nm.algebra.linear.matrix.doubles.operation with parameters of type BivariateRealFunction Modifier and Type Method Description static Matrix
MatrixUtils. elementOperation(Matrix A1, Matrix A2, BivariateRealFunction f)
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Uses of BivariateRealFunction in dev.nm.analysis.differentialequation.pde.finitedifference.elliptic.dim2
Constructors in dev.nm.analysis.differentialequation.pde.finitedifference.elliptic.dim2 with parameters of type BivariateRealFunction Constructor Description PoissonEquation2D(double a, double b, BivariateRealFunction f, BivariateRealFunction g)
Constructs a Poisson's equation problem. -
Uses of BivariateRealFunction in dev.nm.analysis.differentialequation.pde.finitedifference.hyperbolic.dim2
Constructors in dev.nm.analysis.differentialequation.pde.finitedifference.hyperbolic.dim2 with parameters of type BivariateRealFunction Constructor Description WaveEquation2D(double beta, double T, double a, double b, BivariateRealFunction f, BivariateRealFunction g)
Create a two-dimensional wave equation. -
Uses of BivariateRealFunction in dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation
Constructors in dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation with parameters of type BivariateRealFunction Constructor Description ConvectionDiffusionEquation1D(BivariateRealFunction sigma, BivariateRealFunction mu, BivariateRealFunction R, double a, double T, UnivariateRealFunction f, double c1, UnivariateRealFunction g1, double c2, UnivariateRealFunction g2)
Constructs a convection-diffusion equation problem. -
Uses of BivariateRealFunction in dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim2
Constructors in dev.nm.analysis.differentialequation.pde.finitedifference.parabolic.dim2 with parameters of type BivariateRealFunction Constructor Description HeatEquation2D(double beta, double T, double a, double b, BivariateRealFunction f, TrivariateRealFunction g)
Constructs a two-dimensional heat equation problem. -
Uses of BivariateRealFunction in dev.nm.analysis.differentiation.univariate
Classes in dev.nm.analysis.differentiation.univariate that implement BivariateRealFunction Modifier and Type Class Description class
DBeta
This is the first order derivative function of theBeta
function w.r.t x, \({\partial \over \partial x} \mathrm{B}(x, y)\). -
Uses of BivariateRealFunction in dev.nm.analysis.function.rn2r1
Classes in dev.nm.analysis.function.rn2r1 that implement BivariateRealFunction Modifier and Type Class Description class
AbstractBivariateRealFunction
A bivariate real function takes two real arguments and outputs one real value. -
Uses of BivariateRealFunction in dev.nm.analysis.function.special.beta
Classes in dev.nm.analysis.function.special.beta that implement BivariateRealFunction Modifier and Type Class Description class
Beta
The beta function defined as: \[ B(x,y) = \frac{\Gamma(x)\Gamma(y)}{\Gamma(x+y)}= \int_0^1t^{x-1}(1-t)^{y-1}\,dt, x > 0, y > 0 \]class
LogBeta
This class represents the log of Beta functionlog(B(x, y))
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Uses of BivariateRealFunction in dev.nm.analysis.function.special.gamma
Classes in dev.nm.analysis.function.special.gamma that implement BivariateRealFunction Modifier and Type Class Description class
GammaLowerIncomplete
The Lower Incomplete Gamma function is defined as: \[ \gamma(s,x) = \int_0^x t^{s-1}\,e^{-t}\,{\rm d}t = P(s,x)\Gamma(s) \] P(s,x) is the Regularized Incomplete Gamma P function.class
GammaRegularizedP
The Regularized Incomplete Gamma P function is defined as: \[ P(s,x) = \frac{\gamma(s,x)}{\Gamma(s)} = 1 - Q(s,x), s \geq 0, x \geq 0 \]class
GammaRegularizedPInverse
The inverse of the Regularized Incomplete Gamma P function is defined as: \[ x = P^{-1}(s,u), 0 \geq u \geq 1 \] Whens > 1
, we use the asymptotic inversion method. Whens <= 1
, we use an approximation of P(s,x) together with a higher-order Newton like method. In both cases, the estimated value is then improved using Halley's method, c.f.,HalleyRoot
.class
GammaRegularizedQ
The Regularized Incomplete Gamma Q function is defined as: \[ Q(s,x)=\frac{\Gamma(s,x)}{\Gamma(s)}=1-P(s,x), s \geq 0, x \geq 0 \] The algorithm used for computing the regularized incomplete Gamma Q function depends on the values of s and x.class
GammaUpperIncomplete
The Upper Incomplete Gamma function is defined as: \[ \Gamma(s,x) = \int_x^{\infty} t^{s-1}\,e^{-t}\,{\rm d}t = Q(s,x) \times \Gamma(s) \] The integrand has the same form as the Gamma function, but the lower limit of the integration is a variable. -
Uses of BivariateRealFunction in dev.nm.stat.timeseries.linear.univariate
Classes in dev.nm.stat.timeseries.linear.univariate that implement BivariateRealFunction Modifier and Type Class Description class
AutoCorrelationFunction
This is the auto-correlation function of a univariate time series {xt}.class
AutoCovarianceFunction
This is the auto-covariance function of a univariate time series {xt}. -
Uses of BivariateRealFunction in dev.nm.stat.timeseries.linear.univariate.sample
Classes in dev.nm.stat.timeseries.linear.univariate.sample that implement BivariateRealFunction Modifier and Type Class Description class
SampleAutoCorrelation
This is the sample Auto-Correlation Function (ACF) for a univariate data set.class
SampleAutoCovariance
This is the sample Auto-Covariance Function (ACVF) for a univariate data set.class
SamplePartialAutoCorrelation
This is the sample partial Auto-Correlation Function (PACF) for a univariate data set. -
Uses of BivariateRealFunction in dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma
Classes in dev.nm.stat.timeseries.linear.univariate.stationaryprocess.arma that implement BivariateRealFunction Modifier and Type Class Description class
AutoCorrelation
Compute the Auto-Correlation Function (ACF) for an AutoRegressive Moving Average (ARMA) model, assuming that EXt = 0.class
AutoCovariance
Computes the Auto-CoVariance Function (ACVF) for an AutoRegressive Moving Average (ARMA) model by recursion.
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