ll777
- 2
- 0
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.
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.
Last edited by a moderator: