Distribution of sum of moments

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SUMMARY

The discussion focuses on calculating the probability of a horizontal beam tipping to the left when weights are placed on it. Two cases are analyzed: Case A, where each weight has a mass of 1, and Case B, where weights to the left of the pivot have a mass of b > 1. In Case A, the probability is derived using the triangular distribution of the sum of uniform random variables. In Case B, the approach involves convolutions of distributions and conditional probabilities to determine the tipping probability based on the number of weights (n), the pivot position (α), and the mass (b).

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ll777
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Suppose there is a horizontal beam with length 1.

There is a pivot at distance \alpha from the left end of the beam.

The beam is held in place, and n weights are placed on it. The positions of the weights (x1,..,xn) are independent and drawn uniformly from the interval [0, 1]. The beam is released, and either tips to the left or the right. I am interested in the probability that the beam tips to the left in terms of n and \alpha when n is small.

Case A: Each weight has a mass of 1.

Here, I think the problem can be solved using the http://mathworld.wolfram.com/UniformSumDistribution.html" . When n=1, the beam tips left if the weight is left of the pivot. So, p(tips left)= p(x1<\alpha)=\alpha. When n=2, it tips left if x1 + x2 < \alpha. Let x1 + x2 = z. Since z is the sum of two uniform random variables, z has a triangular distribution, and this can be used to find p(tips left). Etc.

Case B: Everything is as above except that each weight to the left of the pivot is replaced with a weight of mass b > 1.

I want to find the probability the beams tips left in terms of n, \alpha , and b. I am not sure of the best approach. One option is to repeatedly calculate convolutions, i.e. the distribution of the sum of moments for n + 1 is a convolution of the distributions for n and distribution of the moment of the (n+1)th weight, but I wonder if there is another way. Any suggestions would be much appreciated.
 
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ll777 said:
Case B: Everything is as above except that each weight to the left of the pivot is replaced with a weight of mass b > 1.

I want to find the probability the beams tips left in terms of n, \alpha , and b. I am not sure of the best approach. One option is to repeatedly calculate convolutions, i.e. the distribution of the sum of moments for n + 1 is a convolution of the distributions for n and distribution of the moment of the (n+1)th weight, but I wonder if there is another way. Any suggestions would be much appreciated.

It could be done via conditional probabilities. Given that N points landed to the left of alpha, the combined moment about alpha could be written as M=-b*alpha*(U(1)+...+U(N))+(1-alpha)*(U(N+1)+...+U(n)) where U(1),...,U(n) are iid uniform.
 

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