Most Probable Value of 3 fair choices

In summary, the problem asks to find the most probable value for a 3-sided coin with N number of atoms in each state. The student attempted to find the solution by maximizing the log of the combinatorial thing with the constraint x+y+z=N.
  • #1
Poop-Loops
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Homework Statement



Okay, so the problem asks to show that if you have N number of O2 molecules with spin either 1, 0, or -1, the most probable value for either of those is N/3, assuming they don't interact at all and no B field, so basically 3-sided coins.

Homework Equations



Asked to start with the multinomial coefficient formula:
http://en.wikipedia.org/wiki/Multinomial_theorem

And Stirling's crude approximation for N! = N*ln(N) - N

The Attempt at a Solution



So Probability = [tex]\frac{\Omega}{\Omega_t}[/tex], right?

In my case [tex]\Omega_t = 3^N[/tex], so I substituted that. And for [tex]\Omega[/tex] I substituted the multinomial expansion coefficient formula, so I got my probability. Then I simplified the factorial on top and the ones in Product function on the bottom of the fraction and then multiplied them out, since I'm only dealing with 3 choices which I label x,y,z for clarity, so they are easy to multiply out.

Now I'm stuck. I want to take the derivative of this and set it to 0, but I'm not sure what to take the derivative with respect to. N? Also, I'm still stuck with an x, y, and z, but I'm thinking I can set those equal to 1.

[tex](x+y+z)^N = 3^N => x=y=z=1[/tex]

But that also seems dubious to me.
 
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  • #2
I really hate to bump my own thread, especially since it's only been a day, but this thread has been buried already and I haven't figured out my problem yet. :frown:
 
  • #3
If the number of atoms in each state is k1, k2 and k3, the usual way to handle this is to treat the constraint that (k1+k2+k3)=N as a Lagrange multiplier along with the crude approximation log(k!)=k*log(k). Can you handle that?
 
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  • #4
Wow! I haven't used Lagrange multipliers in... well over a year. But yeah, if it's as simple as you say then I'll have no problem because I've used them before.

Yes, now I see what you are saying. I think I'll be fine now. Thanks. :)
 
  • #5
I spent several hours today trying to figure it out and I still can't.

What I'm trying to do is this:

[tex]\frac{N!}{(3^N)(x!)(y!)(z!)} + \lambda(x + y + z - N) = F[/tex]

Right? I'm trying to maximize the probability (first part) with the constraint that x + y + z = N.

Then I'd do the Lagrange Multipliers by taking partial derivatives and setting them to 0, and solving for the variables. Well I don't know where the natural log comes into play here. If I try to take the natural log of both sides and then take the partials... I just get a huge mess and none of it makes any sense...

I thought about substituting for example x with N - y - z, but that still doesn't help me much.
 
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  • #6
You've got the setup right. Now let's maximize the log of the combinatorial thing instead of maximizing the thing. Same deal, right? Use Stirling to write this to the highest order as: constant*(x*ln(x)+y*ln(y)+z*ln(z))-lambda*(x+y+z-N)=F

dF/dx=0 gives constant*(1+ln(x))-lambda=0.
dF/dy=0 gives constant*(1+ln(y))-lambda=0.
...

Right? What does this tell you about the relation between x and y?
 
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  • #7
Yes, that's what I did at first, but then I noticed that taking the log of the combinatorial would mean I have to take the log of everything, including the [tex]\lambda[/tex] term and the F, so I'd end up with ln(F).

EDIT: UNLESS... you are saying that I can arbitrarily (it's my choice) decide to maximize the Log of the combinatorial instead of the comb. by itself, with the same constraint in place.
 
  • #8
You don't want to do that. As you've observed, it leads to a complete mess. If you want to maximize G(x,y,z) subject to the constraint x+y+z=N, you can equally well choose to maximize ln(G(x,y,z)). It's called entropy.
 
  • #9
Yes, thank you. I will proceed to do that now. I sort of remember Entropy from my thermodynamics class, but we haven't gotten to it yet in my stat mech class so I didn't think of it at all.

Thanks again for your help. :D
 

1. What is the meaning of "Most Probable Value" in relation to 3 fair choices?

The most probable value in this context refers to the choice that is most likely to occur out of three fair options. It is the outcome with the highest probability of occurring.

2. How is the most probable value of 3 fair choices determined?

The most probable value of 3 fair choices is determined by calculating the probability of each choice and selecting the one with the highest probability. This can be calculated using mathematical formulas or through experimentation and data analysis.

3. Can the most probable value of 3 fair choices change over time?

Yes, the most probable value of 3 fair choices can change over time. This can be due to external factors that affect the probability of each choice, or through the use of different calculation methods that result in a different most probable value.

4. How is the most probable value of 3 fair choices different from the average or median?

The most probable value of 3 fair choices is different from the average or median because it is based on the probability of each choice occurring, rather than the numerical value of each choice. It is the outcome that is most likely to occur, rather than the middle or average value of the options.

5. Can the most probable value of 3 fair choices be used to predict future outcomes?

Yes, the most probable value of 3 fair choices can be used to make predictions about future outcomes. However, it is important to note that this value is not a guarantee and can change depending on various factors. It is just the most likely outcome based on the given options.

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