sigma=1; % standard deviation
figure
for mu=-10:2:10   % mean  
xmax=mu+10*sigma;
xmin=mu-10*sigma;
N=500;
dx=(xmax-xmin)/N;
x=[ xmin+dx/2:dx:xmax-dx/2];
fact=1/sqrt(2*pi*sigma^2);
z=(x-mu)/sigma ; % normalized variable
f=fact*exp(-0.5*z.^2); % Gaussian 
plot(x,f,'.b')
hold on 
end