Probability density for related variables

In summary, the conversation is discussing the calculation of a probability density for a system with m spins up and N total particles, with equal probabilities for up and down states. The magnetization is defined as 2m-N and the task is to calculate the probability density of M. The equation ##\omega_m(m)=\frac{1}{2^N}\frac{N!}{m!(N-m)!}## is mentioned as a starting point, but there is some confusion about how to interpret a probability density for M. It is suggested to replace m with (M+N)/2, taking into account the parity of the system. Overall, it is determined that the solution obtained by rewriting ##\omega## as ##\omega(N
  • #1
diegzumillo
174
18

Homework Statement


Say I calculated a probability density of a system containing m spins up (N is the total number of particles). The probabilities of being up and down are equal so this is easy to calculate. Let's call it ##\omega_m##. Then we define magnetization as ##M=2m-N## and it asks me to calculate the probability density of M.

Homework Equations


##\omega_m(m)=\frac{1}{2^N}\frac{N!}{m!(N-m)!}##

The Attempt at a Solution


I'm not sure how to interpret a probability density of M. M can have a value between -N and N, so the probability, for example, of M=-N is the same as m=0. This suggests me that I can simply replace m for (M+N)/2 in its probability density expression. I don't know if that makes sense, the mathematical properties of these probability densities are no where to be found (at least not with this particular detail).
 
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  • #2
I think my comprehensioon of that probability density improved a bit. It's the probability of the system to be in the state defined by N and m (or M), so my solution obtained from simply rewriting ##\omega## as ##\omega(N,M)## is right. Right?
 
  • #3
diegzumillo said:
This suggests me that I can simply replace m for (M+N)/2 in its probability density expression.
Yes, except consider parity.
 

FAQ: Probability density for related variables

What is probability density for related variables?

Probability density for related variables is a statistical concept that is used to describe the likelihood of a certain value occurring for a set of related variables. It is often used in fields such as physics, engineering, and economics to model the behavior of complex systems.

How is probability density for related variables calculated?

The calculation of probability density for related variables involves determining the function that represents the relationship between the variables, and then using integrals to find the area under the curve of this function. This area represents the probability of a particular value occurring for the related variables.

What is the difference between probability density and probability?

Probability density is a continuous measure of the likelihood of a value occurring, while probability is a discrete measure of the likelihood of an event occurring. Probability density is used for continuous variables, while probability is used for discrete variables.

What is the significance of probability density for related variables?

Probability density for related variables is important because it allows us to make predictions about the behavior of complex systems. It also enables us to quantify the uncertainty associated with these predictions, which is crucial in decision-making processes.

What are some real-world applications of probability density for related variables?

Probability density for related variables has a wide range of applications, including predicting stock market trends, modeling weather patterns, and analyzing the behavior of subatomic particles. It is also used in fields such as machine learning, risk analysis, and genetics.

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