Simplex Point Picking: Distribution of x_i Over (0,1)

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In summary, Skinnerd suggests to pick individual members of (y_{1},\ldots,y_{n}) randomly from a uniform distribution over the interval (0,1) and then take x_{i}=\frac{\ln{}y_{i}}{\sum{}\ln{}y_{i}}. So far so good (although, why does he need the minus sign in his x_{i}=-\ln{}y_{i}?).
  • #1
noowutah
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I have an application where I need to pick a probability distribution [itex](x_{1},\ldots,x_{n})[/itex] at random and uniformly from the simplex of all points for which the coordinates add up to 1, i.e. [tex]\sum_{i=1}^{n}x_{i}=1.[/tex] Surprisingly, I didn't find much about simplex point picking on the internet, but http://en.wikipedia.org/wiki/User:Skinnerd/Simplex_Point_Picking appears to address this issue. Skinnerd suggests to pick individual members of [itex](y_{1},\ldots,y_{n})[/itex] randomly from a uniform distribution over the interval [itex](0,1)[/itex] and then take [tex]x_{i}=\frac{\ln{}y_{i}}{\sum{}\ln{}y_{i}}.[/tex] So far so good (although, why does he need the minus sign in his [itex]x_{i}=-\ln{}y_{i}[/itex]?).

My question is: what is the distribution of [itex]x_{i}[/itex] over the interval [itex](0,1)[/itex], i.e. what is the probability [itex]P(a<x<b)[/itex] that one of these coordinates is in [itex](a,b)\subseteq{}(0,1)[/itex]?
 
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If Xi = x, that leaves a hyperpyramid ##\Sigma_{i\neq i}X_j = 1 - x##. Can't you make the p.d.f of Xi proportional to the volume of that?
 
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  • #3
volume of n-dimensional simplex

Great idea! I am a little confused about terminology. Hyperpyramid at http://physicsinsights.org/pyramids-1.html seems to mean that the height of the pyramid is the same as the side of the base -- which is not what we want here. We want something more like a generalization for [itex]n[/itex] dimensions of a pentatope, see http://mathworld.wolfram.com/Pentatope.html. Mathworld advises on the volume of a simplex in [itex]n[/itex] dimensions at http://mathworld.wolfram.com/Cayley-MengerDeterminant.html. What haruspex is suggesting, as I see it, is that

[tex]P(0<x<b)=S(\sqrt{2})-S(\sqrt{2}(1-b))[/tex]

where [itex]S(z)[/itex] is the volume of a simplex in [itex]n[/itex] dimensions whose side length is [itex]z[/itex]. In our case, [itex]z=\sqrt{2}[/itex] because [itex]x_{1}+\ldots{}+x_{n}=1[/itex].
 
  • #4
stlukits said:
Great idea! I am a little confused about terminology. Hyperpyramid at http://physicsinsights.org/pyramids-1.html seems to mean that the height of the pyramid is the same as the side of the base -- which is not what we want here. We want something more like a generalization for [itex]n[/itex] dimensions of a pentatope, see http://mathworld.wolfram.com/Pentatope.html.
Seems that simplex is the word I should have used.
Mathworld advises on the volume of a simplex in [itex]n[/itex] dimensions at http://mathworld.wolfram.com/Cayley-MengerDeterminant.html. What haruspex is suggesting, as I see it, is that

[tex]P(0<x<b)=S(\sqrt{2})-S(\sqrt{2}(1-b))[/tex]

where [itex]S(z)[/itex] is the volume of a simplex in [itex]n[/itex] dimensions whose side length is [itex]z[/itex]. In our case, [itex]z=\sqrt{2}[/itex] because [itex]x_{1}+\ldots{}+x_{n}=1[/itex].
Not sure that's quite what I was saying. For a start, there should be a ratio of volumes in there.
I think I'm saying the p.d.f., f(x) = Sn-1((1-x)√2)/Sn(√2), or maybe the subscripts should be n, n+1. You'd then to integrate that to get the interval probability.
 
  • #5
Yes, indeed, it should be a ratio, not a difference. Thanks, haruspex!
 

What is Simplex Point Picking?

Simplex Point Picking is a method used in probability and statistics to generate random points within a simplex, which is a geometric shape with several dimensions. It is commonly used in simulations and modeling.

What is the Distribution of x_i Over (0,1)?

The distribution of x_i over (0,1) refers to the probability distribution of the points generated by Simplex Point Picking. This distribution is typically uniform, meaning that all points within the simplex have an equal chance of being picked.

How is Simplex Point Picking useful in scientific research?

Simplex Point Picking is useful in scientific research because it allows for the generation of random points within a given space, which can be used in simulations and statistical analysis. This method can help researchers understand the behavior of complex systems and make predictions about their outcomes.

What are the limitations of Simplex Point Picking?

One limitation of Simplex Point Picking is that it can only be used in situations where a simplex is a suitable representation of the data or system being studied. Additionally, the points generated may not accurately reflect the true distribution of the data if the simplex is not a good approximation.

Can the distribution of x_i be adjusted in Simplex Point Picking?

Yes, the distribution of x_i can be adjusted in Simplex Point Picking by using different methods or algorithms. For example, instead of generating points with a uniform distribution, other distributions such as normal or exponential can be used to better represent the data or system being studied.

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