Algorithms

for(int i=UpperLimit;i>=LowerLimit;i--)

double time = DateTime.Now.Millisecond;
                double randomnumber = time / 1000;
                Console.WriteLine("random is :{0}",randomnumber.ToString());

What is a good approximation to the inverse of the 
cumulative normal distribution function? Mathematically we can 
write this as: Find X such that Q(X) = p for any 0 < p < 1. 
(Note: this computes an upper tail probability.) Again, it is 
not possible to write this as a closed form expression, so we 
resort to approximations. Because of the symmetry of the normal distribution, 
we need only consider 0 < p < 0.5. If you 
have p > 0.5, then apply the algorithm below to q = 1-p, 
and then negate the value for X obtained. (This approximation 
is also from Abramowitz and Stegun.) 

t = sqrt[ ln(1/p^2) ] 


           c_0 + c_1*t + c_2*t^2 
X = t - ------------------------------ 
         1 + d_1*t + d_2*t^2 + d_3*t^3 


c_0 = 2.515517 
c_1 = 0.802853 
c_2 = 0.010328 
d_1 = 1.432788 
d_2 = 0.189269 
d_3 = 0.001308 

See Abramowitz and Stegun; Press, et al.

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