VB challenge/ help MONTE CARLO INTEGRATION - vb.net

Question

Im trying to create Monte-Carlo simulation that can be used to derive estimates for integration problems (summing up the area under
a curve). Have no idea what to do now and i am stuck
"to solve this problem we generate a number (say n) of random number pairs for x and y between 0 and 1, for each pair we see if the point (x,y) falls above or below the line. We count the number of times this happens (say c). The area under the curve is computed as c/n"
Really confused please help thank you
Function MonteCarlo()
Dim a As Integer
Dim b As Integer
Dim x As Double
Dim func As Double
Dim total As Double
Dim result As Double
Dim j As Integer
Dim N As Integer
Console.WriteLine("Enter a")
a = Console.ReadLine()
Console.WriteLine("Enter b")
b = Console.ReadLine()
Console.WriteLine("Enter n")
N = Console.ReadLine()
For j = 1 To N
'Generate a new number between a and b
x = (b - a) * Rnd()
'Evaluate function at new number
func = (x ^ 2) + (2 * x) + 1
'Add to previous value
total = total + func
Next j
result = (total / N) * (b - a)
Console.WriteLine(result)
Console.ReadLine()
Return result
End Function

Answer 1

You are using the rejection method for MC area under the curve.
Do this:
Divide the range of x into, say, 100 equally-spaced, non-overlapping bins.
For your function y = f(x) = (x ^ 2) + (2 * x) + 1, generate e.g. 10,000 values of y for 10,000 values of x = (b - a) * Rnd().
Count the number of y-values in each bin, and divide by 10,000 to get a "bin probability." --> p(x).
Next, the proper way to randomly simulate your function is to use the rejection method, which goes as follows:
4a. Draw a random x-value using x = (b - a) * Rnd()
4b. Draw a random uniform U(0,1). If U(0,1) is less than p(x) add a count to the bin.
4c. Continue steps 4a-4b 10000 times.
You will now be able to simulate your y=f(x) function using the rejection method.
Overall, you need to master these approaches before you do what you want since it sounds like you have little experience in bin counts, simulation, etc. Area under the curve is always one using this approach, so just be creative for integrating using MC.
Look at some good textbooks on MC integration.