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 MatrixMatrixUtils. elementOperation(Matrix A1, Matrix A2, BivariateRealFunction f) -
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 classDBetaThis is the first order derivative function of theBetafunction 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 classAbstractBivariateRealFunctionA 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 classBetaThe beta function defined as: \[ B(x,y) = \frac{\Gamma(x)\Gamma(y)}{\Gamma(x+y)}= \int_0^1t^{x-1}(1-t)^{y-1}\,dt, x > 0, y > 0 \]classLogBetaThis class represents the log of Beta functionlog(B(x, y)). -
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 classGammaLowerIncompleteThe 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.classGammaRegularizedPThe Regularized Incomplete Gamma P function is defined as: \[ P(s,x) = \frac{\gamma(s,x)}{\Gamma(s)} = 1 - Q(s,x), s \geq 0, x \geq 0 \]classGammaRegularizedPInverseThe 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.classGammaRegularizedQThe 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.classGammaUpperIncompleteThe 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 classAutoCorrelationFunctionThis is the auto-correlation function of a univariate time series {xt}.classAutoCovarianceFunctionThis 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 classSampleAutoCorrelationThis is the sample Auto-Correlation Function (ACF) for a univariate data set.classSampleAutoCovarianceThis is the sample Auto-Covariance Function (ACVF) for a univariate data set.classSamplePartialAutoCorrelationThis 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 classAutoCorrelationCompute the Auto-Correlation Function (ACF) for an AutoRegressive Moving Average (ARMA) model, assuming that EXt = 0.classAutoCovarianceComputes the Auto-CoVariance Function (ACVF) for an AutoRegressive Moving Average (ARMA) model by recursion.
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