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www.eeworm.com/read/140851/13059018
m demgp.m
%DEMGP Demonstrate simple regression using a Gaussian Process.
%
% Description
% The problem consists of one input variable X and one target variable
% T. The values in X are chosen in two separat
www.eeworm.com/read/140851/13059077
m demhmc2.m
%DEMHMC2 Demonstrate Bayesian regression with Hybrid Monte Carlo sampling.
%
% Description
% The problem consists of one input variable X and one target variable
% T with data generated by samplin
www.eeworm.com/read/140851/13059362
m demmlp2.m
%DEMMLP2 Demonstrate simple classification using a multi-layer perceptron
%
% Description
% The problem consists of input data in two dimensions drawn from a
% mixture of three Gaussians: two of w
www.eeworm.com/read/140700/13066000
txt alg051.txt
> restart;
> # EULER'S ALGORITHM 5.1
> #
> # TO APPROXIMATE THE SOLUTION OF THE INITIAL VALUE PROBLEM:
> # Y' = F(T,Y), A
www.eeworm.com/read/140700/13066306
txt alg051.txt
> restart;
> # EULER'S ALGORITHM 5.1
> #
> # TO APPROXIMATE THE SOLUTION OF THE INITIAL VALUE PROBLEM:
> # Y' = F(T,Y), A
www.eeworm.com/read/140697/13066866
m alg112.m
% NONLINEAR SHOOTING ALGORITHM 11.2
%
% To approximate the solution of the nonlinear boundary-value problem
%
% Y'' = F(X,Y,Y'), A
www.eeworm.com/read/140697/13066870
m alg055.m
% ADAMS VARIABLE STEP-SIZE PREDICTOR-CORRECTOR ALGORITHM 5.5
%
% To approximate the solution of the initial value problem
% y' = f( t, y ), a
www.eeworm.com/read/140697/13066888
m alg051.m
% EULER'S ALGORITHM 5.1
%
% TO APPROXIMATE THE SOLUTION OF THE INITIAL VALUE PROBLEM:
% Y' = F(T,Y), A
www.eeworm.com/read/140697/13067066
m alg112.m
% NONLINEAR SHOOTING ALGORITHM 11.2
%
% To approximate the solution of the nonlinear boundary-value problem
%
% Y'' = F(X,Y,Y'), A
www.eeworm.com/read/140697/13067074
m alg055.m
% ADAMS VARIABLE STEP-SIZE PREDICTOR-CORRECTOR ALGORITHM 5.5
%
% To approximate the solution of the initial value problem
% y' = f( t, y ), a