Exponential random numbers
R = distfun_exprnd ( mu ) R = distfun_exprnd ( mu , v ) R = distfun_exprnd ( mu , m , n )
a matrix of doubles, the average. Must be positive.
a 1x2 or 2x1 matrix of doubles, the size of R
the number of rows of R
the number of columns of R
a 1x1 matrix of floating point integers, the number of rows of R
a 1x1 matrix of floating point integers, the number of columns of R
a matrix of doubles, the random numbers.
Generates random variables by inversion of the Beta cumulated probability distribution function.
Any scalar input argument is expanded to a matrix of doubles of the same size as the other input arguments.
As a side effect, it modifies the internal seed of the grand function.
Notice that mu, the average, is the inverse of the rate. Other computing languages (including R), use 1/mu as the parameter of the exponential distribution.
// Set the seed so as to always get the same results. distfun_seedset(1); // Use R = distfun_exprnd ( mu ) distfun_exprnd(1:6) // Check mean and variance for R = distfun_exprnd ( mu ) N = 1000; for mu = 1:6; computed = distfun_exprnd(mu,[1 N]); disp(mu) c = mean(computed) e = mu c = st_deviation(computed) e = mu end // Check R = distfun_exprnd ( mu , v ) computed = distfun_exprnd(2,[1 5]) computed = distfun_exprnd(2,[3 2]) // Check mean and variance for R = distfun_exprnd ( a , b ) N = 1000; mu = 2; computed = distfun_exprnd(mu,[1 N]); c = mean(computed(1:N) ) e = mu c = st_deviation(computed(1:N) ) e = mu // Check R = distfun_exprnd ( mu , m , n ) computed = distfun_exprnd([1 2 3;4 5 6],2,3) computed = distfun_exprnd(2,2,3) computed = distfun_exprnd(1,2,3) // Check mean and variance for R = distfun_exprnd ( mu ) N = 1000; mu = 2; computed = distfun_exprnd(mu,1,N); c = mean(computed(1:N) ) e = mu c = st_deviation(computed(1:N) ) e = mu // Make a plot of the actual distribution of the numbers mu = 2; data = distfun_exprnd(mu,1,1000); histplot(10,data) x = linspace(0,14,1000); y = distfun_exppdf(x,mu); plot(x,y) | ![]() | ![]() |