Algorithm 2 Uniform Discretization Algorithm, UDA
Step 1.	Initialize the number of sample points K. Set $H=0$, $s=0$, and $k={\displaystyle \frac{K}{2}}$.
Step 2.	Set $s=s+1$ and calculate $H=H+(-tlnt-(1-t)ln(1-t))w_s$, where $t={\displaystyle \frac{1}{2}}{\mu }_1(x_{1s}^L)$, $w_s=f({\mathbf{X}}_s^L)-f({\mathbf{X}}_{s-1}^L)$.
Step 3.	If $s\le k$, go to Step 2. Otherwise, set $s=0$ and go to Step 4.
Step 4.	Set $s=s+1$ and calculate $H=H+(-tlnt-(1-t)ln(1-t))w_{k-s}$, where $t={\displaystyle \frac{1}{2}}{\mu }_1(x_{1s}^R)$, $w_{k-s}=f({\mathbf{X}}_s^R)-f({\mathbf{X}}_{s-1}^R)$.
Step 5.	If $s\le k$, go to Step 4. Otherwise, go to Step 6.
Step 6.	Calculate $H=H+S(0.5)w_k$, where $w_k=f({\mathbf{X}}_k^R)-f({\mathbf{X}}_k^L)$.
Step 7.	Return H as the estimated value of $H[f({\xi }_1,{\xi }_2,\cdots ,{\xi }_n)]$.