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bn.
b as in
f(x) = a * exp{-(x - b)2 / 2 / c2}
B matrix.
b as in A * x ≤ b
b as in A * x ≥ b
H22 such that H22 is the largest unreduced Hessenberg sub-matrix.
- backward(Vector) -
Method in class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.stationary.SorSweep
- Perform a backward sweep.
- Backward - Class in com.numericalmethod.suanshu.stats.regression.linear.modelselection
- To construct a GLM model for a set of observations using the backward selection method,
we first assume that all factors are included in the model.
- Backward(GlmProblem, double) -
Constructor for class com.numericalmethod.suanshu.stats.regression.linear.modelselection.Backward
- Construct automatically a GLM model using the backward selection method.
- BackwardSubstitution - Class in com.numericalmethod.suanshu.matrix.doubles.linearsystem
- Backward substitution solves a matrix equation in the form
Ux = b
by an iterative process for an upper triangular matrix U. - BackwardSubstitution(UpperTriangularMatrix) -
Constructor for class com.numericalmethod.suanshu.matrix.doubles.linearsystem.BackwardSubstitution
- Construct a BackwardSubstitution instance to solve for different
Vector b's.
- BanachSpace<B,F extends Field<F> & java.lang.Comparable<F>> - Interface in com.numericalmethod.suanshu.mathstructure
- This interface represents a Banach space.
- Bartlett - Class in com.numericalmethod.suanshu.stats.test.variance
- Bartlett's test is used to test if
k samples are from populations with equal variances, hence homoscedasticity. - Bartlett(double[]...) -
Constructor for class com.numericalmethod.suanshu.stats.test.variance.Bartlett
- Perform the Bartlett test to test if the samples are from populations with equal variances.
- base -
Variable in class com.numericalmethod.suanshu.matrix.doubles.operation.Pow
- the radix or base refers to the number
b in an expression of the form bn
- basis() -
Method in class com.numericalmethod.suanshu.matrix.doubles.linearsystem.Kernel
- Get a copy of the basis of the kernel.
- basis -
Variable in class com.numericalmethod.suanshu.matrix.doubles.linearsystem.Solver
- the basis of
A; solution for the homogeneous part, Ax = 0
- Basis - Class in com.numericalmethod.suanshu.vector.doubles.dense.operation
- A basis is a set of linearly independent vectors spanning a vector space.
- Basis(int, int) -
Constructor for class com.numericalmethod.suanshu.vector.doubles.dense.operation.Basis
- Construct a vector which corresponds to the i-th dimension in Rn.
- basis(int) -
Static method in class com.numericalmethod.suanshu.vector.doubles.dense.operation.Basis
- Get the full set of standard basis vectors.
- basis(int, int) -
Static method in class com.numericalmethod.suanshu.vector.doubles.dense.operation.Basis
- Get a subset of standard basis vectors.
- basis() -
Method in class com.numericalmethod.suanshu.vector.doubles.dense.operation.VectorSpace
- Get a copy of the orthogonal basis.
- basisAndFreeVars() -
Method in class com.numericalmethod.suanshu.matrix.doubles.linearsystem.Kernel
- Get a copy of the basis of the kernel and the associated free variables for each basis/column.
- begin -
Variable in class com.numericalmethod.suanshu.interval.Interval
- the beginning of this interval
- BEGINNING_OF_TIME -
Static variable in class com.numericalmethod.suanshu.time.JodaTimeUtils
- This represents a time before all (representable) times.
- BEGINNING_OF_TIME_LONG -
Static variable in class com.numericalmethod.suanshu.time.JodaTimeUtils
- This represents a time before all (representable) times, in long representation.
- Bessel - Class in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde
- This implement the Bessel Process, sum of squared Brownian motions, using the multi-dimensional SDE.
- Bessel(int) -
Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.Bessel
- Construct a Bessel process.
- Bessel - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde
- This implement the Bessel Process, sum of squared Brownian motions, using the 1-dimensional SDE.
- Bessel(int) -
Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.Bessel
- Construct a Bessel process.
- Beta - Class in com.numericalmethod.suanshu.analysis.function.special
- This class represents the Beta function
B(x, y). - Beta() -
Constructor for class com.numericalmethod.suanshu.analysis.function.special.Beta
-
- beta() -
Method in class com.numericalmethod.suanshu.stats.cointegration.CointegrationMle
- Get the set of cointegrating factors.
- beta(int) -
Method in class com.numericalmethod.suanshu.stats.cointegration.CointegrationMle
- Get the r-th cointegrating factor, counting from 1.
- beta -
Variable in class com.numericalmethod.suanshu.stats.distribution.univariate.BetaDistribution
- β: the shape parameter
- Beta - Class in com.numericalmethod.suanshu.stats.regression.linear
- Beta coefficients are the outcomes of fitting a linear regression model.
- Beta(Vector, Matrix, Vector) -
Constructor for class com.numericalmethod.suanshu.stats.regression.linear.Beta
- Construct an instance of Beta.
- Beta - Class in com.numericalmethod.suanshu.stats.regression.linear.glm
- This class represents the estimates of the beta in a Generalized Linear Model.
- Beta(Vector, Matrix) -
Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.Beta
- Construct an instance of Beta.
- beta -
Variable in class com.numericalmethod.suanshu.stats.regression.linear.glm.GeneralizedLinearModel
- the GLM coefficients β^ statistics
- Beta - Class in com.numericalmethod.suanshu.stats.regression.linear.glm.quasi
- This class represents the estimates of the beta in a quasi Generalized Linear Model,
i.e., a GLM with a quasi-family.
- beta -
Variable in class com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.GeneralizedLinearModelQuasiFamily
- the GLM coefficients β^ statistics
- Beta - Class in com.numericalmethod.suanshu.stats.regression.linear.logistic
- Beta coefficient estimates, β^, of a logistic regression model.
- beta -
Variable in class com.numericalmethod.suanshu.stats.regression.linear.logistic.Logistic
- the β^ statistics
- Beta - Class in com.numericalmethod.suanshu.stats.regression.linear.ols
- Beta coefficient estimates, β^, of an Ordinary Least Square linear regression model.
- Beta(Vector, Matrix) -
Constructor for class com.numericalmethod.suanshu.stats.regression.linear.ols.Beta
- Construct an instance of Beta.
- beta -
Variable in class com.numericalmethod.suanshu.stats.regression.linear.ols.OlsRegression
- the β^ statistics
- beta() -
Method in class com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch.GarchModel
- Get the GARCH coefficients.
- BetaDistribution - Class in com.numericalmethod.suanshu.stats.distribution.univariate
- BetaDistribution distribution is the posterior distribution of the parameter
p of a binomial distribution
after observing α − 1 independent events with probability p and
β − 1 with probability 1 − p,
if the prior distribution of p is uniform. - BetaDistribution(double, double) -
Constructor for class com.numericalmethod.suanshu.stats.distribution.univariate.BetaDistribution
- Construct a Beta distribution.
- betaHat -
Variable in class com.numericalmethod.suanshu.stats.regression.linear.Beta
- the coefficient estimates, β^
- betaHat() -
Method in interface com.numericalmethod.suanshu.stats.regression.linear.glm.Fitting
- Get the estimates of β, β^, as in
E(Y) = μ = g-1(Xβ)
- betaHat() -
Method in class com.numericalmethod.suanshu.stats.regression.linear.glm.IWLS
-
- BetaRegularized - Class in com.numericalmethod.suanshu.analysis.function.special
- This class represents the Regularized Incomplete Beta function
Bx(p, q). - BetaRegularized(double, double) -
Constructor for class com.numericalmethod.suanshu.analysis.function.special.BetaRegularized
- Construct an instance of
Bx(p, q) with the parameters p and q.
- BetaRegularizedInverse - Class in com.numericalmethod.suanshu.analysis.function.special
- This class computes the inverse of the Regularized Incomplete Beta function.
- BetaRegularizedInverse(double, double) -
Constructor for class com.numericalmethod.suanshu.analysis.function.special.BetaRegularizedInverse
- Construct an instance of
B-1(p, q)(u) with the parameters p and q.
- BFGS - Class in com.numericalmethod.suanshu.optimization.unconstrained.quasinewton
- The Broyden-Fletcher-Goldfarb-Shanno method is a quasi-Newton method
to solve unconstrained nonlinear optimization problems.
- BFGS() -
Constructor for class com.numericalmethod.suanshu.optimization.unconstrained.quasinewton.BFGS
-
- BIC -
Variable in class com.numericalmethod.suanshu.stats.regression.linear.ols.InformationCriteria
- Bayesian information criterion
- BiconjugateGradientSolver - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary
- The Biconjugate Gradient method (BiCG) is useful for solving non-symmetric
n-by-n linear systems.
- BiconjugateGradientSolver() -
Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.BiconjugateGradientSolver
-
- BiconjugateGradientSolver(int) -
Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.BiconjugateGradientSolver
- The solver recomputes the residual as b - Axi once per this number of iterations
- BiconjugateGradientStabilizedSolver - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary
- The Biconjugate Gradient Stabilized (BiCGSTAB) method is useful for solving
non-symmetric n-by-n linear systems.
- BiconjugateGradientStabilizedSolver() -
Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.BiconjugateGradientStabilizedSolver
-
- BiconjugateGradientStabilizedSolver(int) -
Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary.BiconjugateGradientStabilizedSolver
- The solver recomputes the residual as b - Axi once per this number of iterations
- Bidiagonalization - Class in com.numericalmethod.suanshu.matrix.doubles.factorization.diagonalization
- Given a tall (
m x n) matrix A, where m ≥ n,
we find orthogonal matrices U and V such that
U' %*% A %*% V = B
B is an upper bi-diagonal matrix. - Bidiagonalization(Matrix) -
Constructor for class com.numericalmethod.suanshu.matrix.doubles.factorization.diagonalization.Bidiagonalization
- Run the Householder bidiagonalization for a tall matrix.
- BidiagonalMatrix - Class in com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal
- This class represents a matrix with non-zero entries only on the main, and either the super-diagonal or sub-diagonal.
- BidiagonalMatrix(double[][]) -
Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix
- Construct a bidiagonal matrix from a 2D double[][] array.
- BidiagonalMatrix(int, BidiagonalMatrix.Type) -
Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix
- Construct a bidiagonal matrix of dimension
dim * dim.
- BidiagonalMatrix(BidiagonalMatrix) -
Constructor for class com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal.BidiagonalMatrix
- Copy constructor.
- BidiagonalMatrix.Type - Enum in com.numericalmethod.suanshu.matrix.doubles.matrixtype.dense.diagonal
- the types of bidiagonal matrices available
- bigDecimal() -
Method in class com.numericalmethod.suanshu.number.Real
- Construct a BigDecimal from this Real number.
- BigDecimalUtils - Class in com.numericalmethod.suanshu.number.big
- This class collects a set of utility functions for the java class BigDecimal.
- BigIntegerUtils - Class in com.numericalmethod.suanshu.number.big
- This class collects a set of utility functions for the java class BigInteger.
- bigN -
Variable in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovDistribution
- the big N for which n > bigN we use the asymptotic distribution
- bigN -
Variable in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovOneSidedDistribution
- the big N for which
n > bigN we use the asymptotic distribution
- bigN -
Variable in class com.numericalmethod.suanshu.stats.test.distribution.kolmogorov.KolmogorovTwoSamplesDistribution
- the big N for which
n > bigN we use the asymptotic distribution
- Binomial - Class in com.numericalmethod.suanshu.stats.regression.linear.glm.distribution
- The Binomial distribution for the error distribution in a GLM model.
- Binomial() -
Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.Binomial
- Construct an instance of
Binomial.
- Binomial(LinkFunction) -
Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.Binomial
- Construct an instance of
Binomial with an overriding link function.
- Binomial - Class in com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family
- The quasi Binomial family of GLM.
- Binomial() -
Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family.Binomial
- Construct an instance of
Binomial.
- Binomial(LinkFunction) -
Constructor for class com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family.Binomial
- Construct an instance of
Binomial with an overriding link function.
- BivariateRealFunction - Class in com.numericalmethod.suanshu.analysis.function.rn2r1
- This abstract class represents a bivariate real function.
- BivariateRealFunction() -
Constructor for class com.numericalmethod.suanshu.analysis.function.rn2r1.BivariateRealFunction
-
- BorderedHessian - Class in com.numericalmethod.suanshu.analysis.differentiation.multivariate
- A bordered Hessian matrix consists of the Hessian of a multivariate function
f,
and the gradient of a multivariate function g. - BorderedHessian(RealScalarFunction, RealScalarFunction, double...) -
Constructor for class com.numericalmethod.suanshu.analysis.differentiation.multivariate.BorderedHessian
- Construct a bordered Hessian matrix for multivariate functions
f and g at point x.
- BoxPierce - Class in com.numericalmethod.suanshu.stats.test.timeseries.portmanteau
- The Box–Pierce test (named for George E.
- BoxPierce(double[], int, int) -
Constructor for class com.numericalmethod.suanshu.stats.test.timeseries.portmanteau.BoxPierce
- Compute the Box–Pierce test statistic for examining the null hypothesis of independence in a given time series.
- BracketSearch - Class in com.numericalmethod.suanshu.optimization.univariate
- This class provides support for the type of univariate optimization algorithms
that is based on bracketing.
- BracketSearch() -
Constructor for class com.numericalmethod.suanshu.optimization.univariate.BracketSearch
-
- Brent - Class in com.numericalmethod.suanshu.analysis.uniroot
- Brent's root-finding algorithm combines superlinear convergence with reliability of bisection.
- Brent(UnivariateRealFunction, double) -
Constructor for class com.numericalmethod.suanshu.analysis.uniroot.Brent
- Construct an instance of Brent's root finding algorithm.
- Brent - Class in com.numericalmethod.suanshu.optimization.univariate
- Brent's algorithm is a root-finding algorithm that combines
the bisection method, the secant method and the inverse quadratic interpolation.
- Brent() -
Constructor for class com.numericalmethod.suanshu.optimization.univariate.Brent
-
- BreuschPagan - Class in com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity
- The Breusch–Pagan test is used to test for heteroskedasticity in a linear regression model.
- BreuschPagan(Residuals, boolean) -
Constructor for class com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity.BreuschPagan
- Perform the Breusch-Pagan test to test for heteroskedasticity in a linear regression model.
- BrownForsythe - Class in com.numericalmethod.suanshu.stats.test.variance
- The Brown–Forsythe test is a statistical test for the equality of group variances based on performing an ANOVA on a transformation of the response variable.
- BrownForsythe(double[]...) -
Constructor for class com.numericalmethod.suanshu.stats.test.variance.BrownForsythe
- Perform the Brown-Forsythe test to test for equal variances of the samples.
- Brownian - Class in com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.brownian
- A multi-variate Brownian motion is a stochastic process with the following properties.
- Brownian(int) -
Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.brownian.Brownian
- Construct a multi-dimensional Brownian motion.
- Brownian(Vector, Matrix) -
Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.brownian.Brownian
- Construct a multi-dimensional Brownian motion with μ and σ.
- Brownian - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.brownian
- A Brownian motion is a stochastic process with the following properties.
- Brownian(double, double) -
Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.brownian.Brownian
- Construct a univariate Brownian motion.
- Brownian() -
Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.brownian.Brownian
- Construct a univariate standard Brownian motion.
- BruteForce - Class in com.numericalmethod.suanshu.optimization.constrained.integer
-
- BruteForce(ConstrainedMinimizerFactory) -
Constructor for class com.numericalmethod.suanshu.optimization.constrained.integer.BruteForce
-
- BruteForce() -
Constructor for class com.numericalmethod.suanshu.optimization.constrained.integer.BruteForce
-
- Bt - Class in com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration
- This is a FiltrationFunction that returns B(t),
the Brownian motion value at the t-th time point.
- Bt() -
Constructor for class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.Bt
-
- Bt() -
Method in class com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration.Filtration
- Get the entire Brownian path.
- build(Vector) -
Method in interface com.numericalmethod.suanshu.optimization.unconstrained.NelderMead.BuildSimplex
- Build a simplex of
N+1 vertices from an initial point.
- buildTable(StandardLpProblem2) -
Static method in class com.numericalmethod.suanshu.optimization.constrained.linearprogramming.simplex.Tableau
- phase 1: (feasible) tableau initialization
|
SuanShu, a Java numerical and statistical library | |||||||
| PREV LETTER NEXT LETTER | FRAMES NO FRAMES | |||||||