Uniform distribution on sphere

In summary, the solution given gives a uniform distribution of points on a sphere, while the solution given does not. To be more specific, the solution given gives x(R1,R2) = s(pi R1, arccos(1-2R2)) while the solution given does not.
  • #1
kaksmet
83
0
Hello

I am trying to make a uniform distribution of points on a sphere. I can find the answer
[tex]\theta=\pi R_1[/tex]
[tex]\phi = arccos(1-2R_2)[/tex]

where R1 and R2 are uniformly distributed random numbers between 0 and 1.

To me, it feels like
[tex]\theta=\pi R_1 sin(R1)[/tex]
[tex]\phi = 2\pi R_2[/tex]
should also give the correct distribution. Is that the case, and if not how can I understand why not this but the above solution is true? How can I calculate my way to the first answer?

thanks!
Tomas
 
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  • #2
Well, you want a parametrization x(u,v) : (0,1) x (0,1) -> the unit sphere S2
such that the unit of area
[tex]|x_u \times x_v|[/tex]
is constant, where [tex]\times[/tex] is the cross product.

It should be simple to verify that this works for the solution given but does not work for your solution. To be more specific, in the solution given,
x(R1,R2) = s(pi R1, arccos(1-2R2))
where s is the function for spherical coordinates, i.e. s(theta, phi) = (sin theta cos phi, sin theta sin phi, cos theta)
 
Last edited:
  • #3
To write theta and phi in terms of uniforms you first need to find their joint density. Since the points are uniformly distributed over the area, the probability of the rectangle [theta,theta+dt] x [phi,phi+dp] is the proportional to the area on the sphere which is
the magnitude of the cross product [dx/dtheta] x [dx/dphi] *(dt*dp).

From this by normalizing you get the joint density f(theta,phi) and integrate to get a marginal density e.g. f(theta) and a conditional density f(phi|theta). The cumulative distribution of any random variable is uniformly distributed, so theta=F^{-1}(R1) and phi=F^{-1}(R2|theta)=F^{-1}(R2|F^{-1}(R1)).

Hope this helps - what answer do you get from this process?
 

1. What is a uniform distribution on a sphere?

A uniform distribution on a sphere is a probability distribution where all points on the surface of a sphere are equally likely to be chosen. This means that no specific area or region of the sphere has a higher chance of being selected than any other area.

2. How is a uniform distribution on a sphere different from a normal distribution?

A normal distribution is a bell-shaped curve that is often used to model natural phenomena. A uniform distribution on a sphere, on the other hand, is not a bell-shaped curve and does not have a mean or standard deviation. It is a discrete distribution that only exists on the surface of a sphere.

3. What is the formula for calculating a point's probability in a uniform distribution on a sphere?

The formula for calculating a point's probability in a uniform distribution on a sphere is P = 1/4πr², where P is the probability, r is the radius of the sphere, and π is the mathematical constant pi.

4. How is a uniform distribution on a sphere used in scientific research?

A uniform distribution on a sphere is used in scientific research to model random processes such as the movement of particles, the distribution of stars and galaxies in the universe, and the behavior of molecules in a gas or liquid. It is also used in computer graphics and simulations to generate random points on a sphere.

5. What are some real-world examples of a uniform distribution on a sphere?

Some real-world examples of a uniform distribution on a sphere include the distribution of stars in a galaxy, the distribution of pollen grains in the air, and the distribution of particles in a liquid. It can also be seen in the pattern of raindrops falling on a surface or the distribution of bubbles in a glass of champagne.

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