Statistical Mechanics - Random Walk

Click For Summary
The discussion centers on understanding the probability of a random walk where a drunk person takes N steps, with n1 steps to the right and n2 steps to the left. The probability of a specific sequence of steps is given by (p^n1)(q^n2), but to find the total probability of ending with n1 steps to the right and n2 steps to the left, one must multiply this by the number of distinct paths, represented by N!/(n1!n2!). This multiplication accounts for all possible sequences leading to the same endpoint, as the order of steps matters in determining the overall probability. The reasoning is further validated through examples and the binomial expansion, demonstrating that the correct formula sums to 1, while (p^n1)(q^n2) alone does not. Understanding this distinction is crucial for grasping the fundamentals of statistical mechanics in random walks.
Fermi-on
Messages
1
Reaction score
0
I'm reading through Reif's "Statistical Mechanics" to prepare for the upcoming semester. Basically, a drunk guy takes N total steps, n1 to the right and n2 to the left. The probability that the current step will be to the right is "p," while the probability that the current step will be to the left is "q=1-p." The probability that one sequence of N steps will have a total of "n1" steps to the right and "n2" steps to the left is "(p^n1)(q^n2)." The total number of possible ways to get that specific number of steps to the right and left after N total steps is "N!/(n1!(N-n1)!) = N!/(n1!n2!)."

I understand that just fine. What I'm stuck on is why you multiply N!/(n1!n2!) and (p^n1)(q^n2) to get the probability of the drunk ending up with n1 steps to the right and n2 steps to the left. Why isn't it just (p^n1)(q^n2)?
 
Physics news on Phys.org
Picture the guy walking on the axis of integers (starting in 0). p^{n_1}q^{n_2} would be the probability of the guy getting to position n_1 - n_2 after N steps, via anyone allowed path. For example, if we keep track of the sequence in which the steps were taken, q \underbrace{p p ... p}_{n_1 \mbox{times}}\underbrace{q q ... q}_{n_2 - 1 \mbox{times}} would represent (the probability of) the path that ends in position n_1 - n_2 by first taking 1 step to the left, then n_1 steps to the right and finally n_2 - 1 more steps to the left. So by writing p^{n_1}q^{n_2} you are only looking at one precise path. But since it doesn't matter which path the guy took, we have to add the probabilities of getting there by all possible paths (if there are 2 ways to get to a point, the probability of getting there would be twice as large as the probability of getting there if there was only 1 way). In this case there are \binom{N}{n_1} paths, each with the same probability p^{n_1}q^{n_2}, so adding this probability up \binom{N}{n_1} times gives the answer.
It might be insightful to check this reasoning in the most simple example: p = q = \frac{1}{2} and n_1 = n_2 = 1. What's the probability that the guy ends up in the origin?
You can also check that this formula defines a probability law, and that just p^{n_1}q^{n_2} alone would not. Given N, the probability to take N steps in total must be equal to 1. But the probability of taking N steps in total is the sum of the probabilities of taking n_1 = 0 steps to the right, n_1 = 1 step to the right, ..., n_1 = N steps to the right. Use the binomial expansion to check that this sums to 1 for the correct formula, but not for just p^{n_1}q^{n_2} alone.
 
For simple comparison, I think the same thought process can be followed as a block slides down a hill, - for block down hill, simple starting PE of mgh to final max KE 0.5mv^2 - comparing PE1 to max KE2 would result in finding the work friction did through the process. efficiency is just 100*KE2/PE1. If a mousetrap car travels along a flat surface, a starting PE of 0.5 k th^2 can be measured and maximum velocity of the car can also be measured. If energy efficiency is defined by...

Similar threads

  • · Replies 1 ·
Replies
1
Views
2K
Replies
3
Views
1K
  • · Replies 3 ·
Replies
3
Views
657
Replies
3
Views
1K
  • · Replies 1 ·
Replies
1
Views
1K
Replies
1
Views
1K
Replies
5
Views
3K
Replies
3
Views
2K
  • · Replies 10 ·
Replies
10
Views
2K
  • · Replies 3 ·
Replies
3
Views
1K