Is this correct the correct code for this? Suppose Xi for i=1, 2, 3… has Uniform(0,1) distribution..

Suppose Xi for i=1, 2, 3… has Uniform(0,1) distribution. Let N = min (n+1: Xn > Xn+1). Find E (N) by simulation. 10,000 simulations required.

For the first part I think this is :

S1 = symsum(x, x, 2, inf); %Summation for Xi for i = 2,3,4...

a = 1; %beta distrubtion Uniform(0,1) distribution with a=1 & b=1

b = 1;

data = S1(a,b); %Xi has Uniform(0,1) distribution

N = (S1) > (S1+1) % setting N

if this is correct then i'm only confused on finding E(N) here..

I was horribly off, thank you for the help here

[HrMmz]

I think i want to change inf to 10,000? getting pretty stuck..

[John D'Errico]

You are supposed to do a simulation. Why are you fooling around with symsum and beta distributions?

Anyway, please explain what you mean by this:

min (n+1: Xn > Xn+1)

It appears that you are making this problem wildly over complex, but if you expect an answer, you need to explain your terminology.

[HrMmz]

Maybe I'll start another question and just ask if anyone can help understand what this means

[John D'Errico]

Asking the same question twice won't get you a better answer.

Answer 1

[Image Analyst]

Like John, I have no idea what "min (n+1: Xn > Xn+1)" means, but anyway, is this what you want?

numSimulations = 10; % or 10000
x = rand(1, numSimulations)
% Make "Xn > Xn+1" by sorting in descending order
x = sort(x, 'descend')  % Now Xn > Xn+1 because we sorted it
% Generate a vector of (n+1) values.
nPlus1 = 2 : (length(x) + 1)
N = min([x; nPlus1], [], 1)
% Of course since all x are less than 2, N will equal x.
en = mean(N)

It's also unclear what the difference between n and i. Are they both simply indexes of the arrays? If so, why did you change notation??? Or does n mean something different than i?

[HrMmz]

Xi and Xn are the same here

thanks again for the help