Distribution of Number of Pieces

In summary, according to the author, the probability that there are at least k disconnected pieces of a given size is given by the equation: P\{C_i = k\} = \frac{1}{(1-v)^i}\binom{i-1}{k-1}\sum_{j=0}^{k-1}(-1)^j\binom{k-1}{j}G(i-k+j, i; v) where G(i-k+j, i; v) is the sum of the probabilities that there are n different pieces at (i-k, i; v) with sizes ranging from m to r. This probability is only
  • #1
mXSCNT
315
1
Let [tex]\{A_i\}[/tex] be independent random variables, real numbers selected uniformly from the interval (0,1-v) for some constant v, 0<v<1.
Let [tex]B_i = \cup^i_{j=1} (A_j,A_j+v)[/tex]
Let [tex]C_i[/tex] be the number of disconnected pieces of [tex]B_i[/tex].

Problem: What is the distribution of [tex]C_i[/tex]? I doubt that a closed form expression is possible but it's tough to even find a computer program to calculate it except via monte carlo.
 
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  • #2
mXSCNT said:
Let [tex]\{A_i\}[/tex] be independent random variables, real numbers selected uniformly from the interval (0,1-v) for some constant v, 0<v<1.
Let [tex]B_i = \cup^i_{j=1} (A_j,A_j+v)[/tex]
Let [tex]C_i[/tex] be the number of disconnected pieces of [tex]B_i[/tex].

Problem: What is the distribution of [tex]C_i[/tex]? I doubt that a closed form expression is possible but it's tough to even find a computer program to calculate it except via monte carlo.

I haven't worked out the details, but I think I see a way to approach this. Even if it doesn't result in a closed form formula, you might be able to work out an algorithm to compute it. Basically, the hard part is finding the volume left of an i-cube of side 1-v after several pieces have been sliced off. It should be easier for v >= 1/3, but tricky for v < 1/3. The idea is to work with the order statistics of the Aj, and find the probability that there are exactly k-1 "gaps" of the form A(m+1)-A(m) > v. In this case Ci = k. Is that making sense?

Mind if I ask what is the application of this?
 
  • #3
Conjecture:

[tex]P\{C_i = k\} = \frac{1}{(1-v)^i}\binom{i-1}{k-1}\sum_{j=0}^{k-1}(-1)^j\binom{k-1}{j}G(i-k+j, i; v)[/tex]

where

[tex]G(m,i;v) = \sum_{n=1}^{r}(-1)^{n+1}\binom{m}{n-1}(1-nv)^i[/tex]

for

[tex]\frac{1}{r+1} \leq v < \frac{1}{r}[/tex]
 

FAQ: Distribution of Number of Pieces

What is the distribution of number of pieces?

The distribution of number of pieces refers to the frequency and pattern of occurrence of different values or categories of a variable in a dataset. It shows how often each value or category appears and gives insight into the overall shape of the data.

How is the distribution of number of pieces represented?

The distribution of number of pieces is typically represented using a histogram, which is a visual representation of the frequency of each value or category. It consists of a series of rectangles where the width represents the range of values and the height represents the frequency.

What factors can affect the distribution of number of pieces?

The distribution of number of pieces can be affected by various factors, such as the type of data (continuous or categorical), the sample size, the presence of outliers, and the underlying population distribution. These factors can impact the shape, center, and spread of the distribution.

What are the different types of distributions for number of pieces?

The most common types of distributions for number of pieces are normal, skewed, and uniform. A normal distribution is symmetrical and bell-shaped, with the majority of values clustered around the mean. Skewed distributions have a longer tail on one side, indicating that the data is not evenly distributed. A uniform distribution has an equal frequency of each value and appears as a flat line on a histogram.

Why is it important to understand the distribution of number of pieces?

Understanding the distribution of number of pieces is important as it allows us to make informed decisions and draw accurate conclusions from the data. It can help identify patterns and trends, detect outliers, and determine the appropriate statistical tests to use. Additionally, it can provide insight into the underlying population and inform the development of future studies or experiments.

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