代码搜索：Approximation

找到约 1,542 项符合「Approximation」的源代码

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c alg041.c

/* * SIMPSON'S COMPOSITE ALGORITHM 4.1 * * To approximate I = integral ( ( f(x) dx ) ) from a to b: * * INPUT: endpoints a, b; even positive integer n. * * OUTPUT: approximation XI t
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www.eeworm.com/read/469123/6977817

m approxla.m

function [alpha, sW, L, nlZ, dnlZ] = approxLA(hyper, covfunc, lik, x, y) % Laplace approximation to the posterior Gaussian Process. % The function takes a specified covariance function (see covFuncti
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www.eeworm.com/read/455746/7366448

m quant.m

function out=quant(data,p,models,first,last,ci,opt), %Creates a plot showing how the estimate of a high quantile in the tail of a dataset %based on the GPD approximation varies with threshold or num
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www.eeworm.com/read/452284/7442642

m fdfir2.m

% MATLAB m-file for approximation of fractional delay % MAIN PROGRAM FOR FIR DESIGN (fdfir2.m) % % Input: Design methods + parameters (all via keyboard) % Output: Magnitude response and
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www.eeworm.com/read/449127/7518001

m simpcomp.m

function [int, S, nfeval] = simpcomp(fun, a, b, tol) % % function [int, nfeval] = simpcomp(f, a, b, tol) % % Compute an approximation of the integral of f over % [a,b] using composite Simpson r
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www.eeworm.com/read/441410/7670766

m model_g.m

function model_g(obs) %MODEL_G The data obs are modeled; obs is assumed to be a row vector! % first by a linear, next by a quadratic approximation. % The model is subtrated from ob
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www.eeworm.com/read/196932/8040055

readme

Bayesian Committee Machine Version 1.0, November 2005 The Bayesian Committee Machine (BCM) is an approximation method for large-scale Gaussian process regression. What you should know beforehand:
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www.eeworm.com/read/140701/13065719

ma alg071.ma

(* JACOBI ITERATIVE ALGORITHM 7.1 * * To solve Ax = b given an initial approximation x(0) * * Input: the number of equations and unknowns n; * the entries A(i,J), 1
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www.eeworm.com/read/140698/13066680

c alg041.c

/* * SIMPSON'S COMPOSITE ALGORITHM 4.1 * * To approximate I = integral ( ( f(x) dx ) ) from a to b: * * INPUT: endpoints a, b; even positive integer n. * * OUTPUT: approximation XI t
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www.eeworm.com/read/140697/13066762

m alg072.m

% GAUSS-SEIDEL ITERATIVE TECHNIQUE ALGORITHM 7.2 % % To solve Ax = b given an initial approximation x(0). % % INPUT: the number of equations and unknowns n; the entries % A(I,J), 1
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