How can I add uncertainty to a data matrix?
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    Wiqas Ahmad
 am 18 Mai 2022
  
    
    
    
    
    Kommentiert: Star Strider
      
      
 am 21 Mai 2022
            I obtained a signal data by running a simulation with input parameters. Some of these inputs are h,a,r,fov as shown in the following program. The output signals are beta_para and beta_per. The output signals are highly influenced by these four input parameters. What I want now is to add uncertainty to the inputs like fov±0.005 and obtained the signals with the input error. One way to do this is to repeat the simulation and input the parameters with uncertainties and obtained the output signals with error, however the process is very lengthy because my data is too big. I had come to know that we can deal it using repmat function in matlab as well without repeating the simulation. I don't know how can I use  the repmat function to add uncertainties of the input parameters h,a,r,and fov to the signals beta_para and beta_per. Please if anybody can help me in this regard. My program is::
clear all,clc,close all
cloud = 'Homo';
T_para = zeros(5, 17, 20);
T_per = zeros(5, 17, 20);
h = 1000:1000:4000;
a = 0.01:0.01:0.05;
r = 4:1:20;
fov = [0.2, 0.5, 1, 2, 5, 10];
for i=1:length(h)
    for j=1:length(fov)
        dir_arhf = ['Tabledata_', cloud, '\', num2str(h(i)), 'm-', num2str(fov(j)), 'mrad'];
        mkdir(dir_arhf);
        for k=1:length(a)
            for m=1:length(r)
                load (['MCdatabase_', cloud, '/', num2str(a(k)), '-', num2str(r(m)), 'um/', num2str(h(i)), 'm-', num2str(fov(j)), 'mrad/I0.mat']);
                load (['MCdatabase_', cloud, '/', num2str(a(k)), '-', num2str(r(m)), 'um/', num2str(h(i)), 'm-', num2str(fov(j)), 'mrad/Q0.mat']);
                I_para = 1/2 * (I0 + Q0);
                I_per = 1/2 * (I0 - Q0);
                hh = genHeight(h(i)).^2;
                beta_para = sum(I_para, 2) .* hh';
                beta_per = sum(I_per, 2) .* hh';
                T_para(a(k) * 100, r(m)-3, :) =beta_para';
                T_per(a(k) * 100, r(m)-3, :) =beta_per';
                save([dir_arhf, '\TABLE.mat'], 'T_per', 'T_para');
            end
        end
    end
end
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  Star Strider
      
      
 am 18 Mai 2022
        The repmat funciton is likely not needed.  Just do something like this: 
h = h + randn(size(h));
and so for the rest.  
.
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