Cashflow (Built-in Mathematica Symbol)

Cashflow[{c_0, c_1, ..., c_n}] represents a series of cash flows occurring at unit time intervals.Cashflow[{c_0, c_1, ..., c_n}, q] represents cash flows occurring at time ...

HalfNormalDistribution (Built-in Mathematica Symbol)

HalfNormalDistribution[\[Theta]] represents a half-normal distribution with scale inversely proportional to parameter \[Theta].

LinearSolve (Built-in Mathematica Symbol)

LinearSolve[m, b] finds an x which solves the matrix equation m.x == b. LinearSolve[m] generates a LinearSolveFunction[...] which can be applied repeatedly to different b.

LQOutputRegulatorGains (Built-in Mathematica Symbol)

LQOutputRegulatorGains[ss, {q, r}] gives the optimal state feedback gain matrix for the StateSpaceModel object ss and the quadratic cost function with output and control ...

MaxwellDistribution (Built-in Mathematica Symbol)

MaxwellDistribution[\[Sigma]] represents a Maxwell distribution with scale parameter \[Sigma].

NakagamiDistribution (Built-in Mathematica Symbol)

NakagamiDistribution[\[Mu], \[Omega]] represents a Nakagami distribution with shape parameter \[Mu] and spread parameter \[Omega].

Graphics Directives and Options (Mathematica Tutorial)

When you set up a graphics object in Mathematica, you typically give a list of graphical elements. You can include in that list graphics directives which specify how ...

DiscreteWaveletPacketTransform (Built-in Mathematica Symbol)

DiscreteWaveletPacketTransform[data] gives the discrete wavelet packet transform (DWPT) of an array of data.DiscreteWaveletPacketTransform[data, wave] gives the discrete ...

Fourier Series Package (Fourier Series Package Tutorial)

The Mathematica kernel provides the functions FourierTransform and InverseFourierTransform for computing the symbolic Fourier exponential transform and inverse transform. It ...

BinormalDistribution (Built-in Mathematica Symbol)

BinormalDistribution[{\[Mu]_1, \[Mu]_2}, {\[Sigma]_1, \[Sigma]_\ 2}, \[Rho]] represents a bivariate normal distribution with mean {\[Mu]_1, \[Mu]_2} and covariance matrix ...