Probability: having trouble with explanations

  • Thread starter terp.asessed
  • Start date
  • Tags
    Probability
In summary, the conversation discusses a random walk where a boy takes n steps and each step is either to the east or west, with a distance of 1m. The n+1 possible positions that the boy can end up at are given by x = 2k-n, where k = 0, 1 ... n. The likelihood of ending up at any of these positions is determined by the probability p(k) = 1/(2^n) (n k). The formula for p(k) is true because of normalization, and (n 0) = 1 because there is only one way to get 0 heads when tossing a coin once. <x> is computed to be 0 because there is an equal
  • #1
terp.asessed
127
3

Homework Statement


I GOT all values...but I have trouble explaining...if someone could suggest how, thank you! Please check the red fonts which shows where I got stuck!

Suppose we call x the position of the boy and each step he takes (east or west ONLY) is of 1m. So, for an "n" step random walk he takes, the n+1 possible positions that the boy can end up at are given by:

x = 2k +n, where k = 0, 1 ...n

and the likelihood of ending up at anyone of these is determined by the probability: p(k) = 1/(2^n) (n k) where (n k) = n! / [(n-k)!(k!)] and k = 0, 1 ... n.

(a) Explain why the formula p(k) should be true.
(b) Compute <x> after an n-step walk. Why should <x> have this value?
(c) compute [<x^2>]^0.5 after n step walk. Suppose all n steps were in the same direction, what would be [<x^2>]^0.5?

Homework Equations


x = 2k +n, where k = 0, 1 ...n
p(k) = 1/(2^n) (n k) where (n k) = n! / [(n-k)!(k!)] and C is a normalization constant and k = 0, 1 ... n.

The Attempt at a Solution


([/B]a) B/c Sigma (k=0 to n) p(k) = 1 for normalization, if n =1, supposing x = -1, k would be equal to 1...giving a p(1) = 1/2 (1/1) = 1/2. This is the explanation I put...but, I don't understand in the case when n=1, x = 1, in which k = 0, giving p(0) = 1/2 (1/0) = ?. Could someone hint why the formula is true?

(b) I obtained the value by:
<k> = Sigma (k=0 to n) k*p(k)
= Sigma k (n k) 1/(2^n)
= 1/(2^n) Sigma k (n k) = 1/(2^n) n * 2^(n-1) = n/2

therefore...<x> = 2<k> - n
= 2(n/2) - n = 0...Why did I get the value of zero, if uncertainty is possible? Is it b/c there is an equal chance of being east or west of the initial position the boy was in the first place?

(c) Similarly, I derived <k^2> which I got n(n+1)/4. So:
<x^2> = <(2k+n)^2> = <4k^2 - 4kn + n^2> = 4<k^2> - 4n<k> + n^2 into which I input the results I had from the above...and got <x^2> = n
So...[<x^2>]^0.5 = n^0.5. However, what should I do if all n steps were in the same direction? Because that is once in all possible probabilities, so, should it be 1/n^0.5? I am really stuck here!
 
Physics news on Phys.org
  • #2
terp.asessed said:

Homework Statement


I GOT all values...but I have trouble explaining...if someone could suggest how, thank you! Please check the red fonts which shows where I got stuck!

Suppose we call x the position of the boy and each step he takes (east or west ONLY) is of 1m. So, for an "n" step random walk he takes, the n+1 possible positions that the boy can end up at are given by:

x = 2k +n, where k = 0, 1 ...n

and the likelihood of ending up at anyone of these is determined by the probability: p(k) = 1/(2^n) (n k) where (n k) = n! / [(n-k)!(k!)] and k = 0, 1 ... n.

(a) Explain why the formula p(k) should be true.
(b) Compute <x> after an n-step walk. Why should <x> have this value?
(c) compute [<x^2>]^0.5 after n step walk. Suppose all n steps were in the same direction, what would be [<x^2>]^0.5?

Homework Equations


x = 2k +n, where k = 0, 1 ...n
p(k) = 1/(2^n) (n k) where (n k) = n! / [(n-k)!(k!)] and C is a normalization constant and k = 0, 1 ... n.

The Attempt at a Solution


([/B]a) B/c Sigma (k=0 to n) p(k) = 1 for normalization, if n =1, supposing x = -1, k would be equal to 1...giving a p(1) = 1/2 (1/1) = 1/2. This is the explanation I put...but, I don't understand in the case when n=1, x = 1, in which k = 0, giving p(0) = 1/2 (1/0) = ?. Could someone hint why the formula is true?


(b) I obtained the value by:
<k> = Sigma (k=0 to n) k*p(k)
= Sigma k (n k) 1/(2^n)
= 1/(2^n) Sigma k (n k) = 1/(2^n) n * 2^(n-1) = n/2

therefore...<x> = 2<k> - n
= 2(n/2) - n = 0...Why did I get the value of zero, if uncertainty is possible? Is it b/c there is an equal chance of being east or west of the initial position the boy was in the first place?

(c) Similarly, I derived <k^2> which I got n(n+1)/4. So:
<x^2> = <(2k+n)^2> = <4k^2 - 4kn + n^2> = 4<k^2> - 4n<k> + n^2 into which I input the results I had from the above...and got <x^2> = n
So...[<x^2>]^0.5 = n^0.5. However, what should I do if all n steps were in the same direction? Because that is once in all possible probabilities, so, should it be 1/n^0.5? I am really stuck here!

The position should be ##x = 2k-n##, not ##2k+n##. Think about it: if he takes ##k## steps to the right and ##n-k## steps to the left, where does he end up?

You say that in (a) you do not understand the formula for n = 1. Which formula do you mean? Do you want to know why (1 0) = 1? Well, that is just the number of ways of getting 0 heads when you toss a coin one time: you get either H (once) or T (once). In other words, there is just one way to get 1 head and 1 way to get 0 heads. In general, (n 0) = 1 for any integer n because when you toss a coin n times there is just one way of getting 0 heads---namely, by getting all tails.

BTW: the standard notation for (a b) is either aCb or C(a,b) or ##\binom{a}{b}##. You get the first one by using the "##x_2##" button on the panel at the top of the input page; you get the second one by straight typing; you get the third one using LaTeX.


In (b) you say you do not understand why ##\langle X \rangle = 0##. Well, what is the MEANING of ##\langle X \rangle ##? It is the average value of X in a large number of identical trials of the situation under study. A mean of 0 just means that going left or right is equally likely. For example, when n = 1 you end up either at x = +1 or x = -1. If you do the experiment 2 million times, then in about 1 million of the experiments you will be at x = 1 and in about 1 million experiments you will be at x = -1. On average, then, your final location will be at zero.

In (c): the value of ##\langle X^2 \rangle## looks at the average of all the possible ##x^2## values. Only one of the very many outcomes has all steps to the right (or all to the left). The contribution of those two extreme outcomes to ##\langle X^2 \rangle## would be ##2 C(n,0) n^2 / 2^n = 2 n^2/2^n## and so is small compared with the total due to other outcomes.
 
  • Like
Likes terp.asessed
  • #3
Hello, thank you very much!
I get (b) and (c)! Also thank you for pointing out how to get the standard notation...I get how to do first two, but, how do I use "Latex"?

Aside, regarding (a), by formula, I mean I have trouble explaining why p(k) = 1/(2n) (n k) where (n k) = n! / [(n-k)!(k!)] is true--should I verify the function with examples? Or, state that it is b/c of normalization? This is where I am confused.
 
  • #4
terp.asessed said:
Hello, thank you very much!
I get (b) and (c)! Also thank you for pointing out how to get the standard notation...I get how to do first two, but, how do I use "Latex"?

Aside, regarding (a), by formula, I mean I have trouble explaining why p(k) = 1/(2n) (n k) where (n k) = n! / [(n-k)!(k!)] is true--should I verify the function with examples? Or, state that it is b/c of normalization? This is where I am confused.
You may also be under the misapprehension that 0! = 0. By convention, 0!=1. This makes sense because it means that the rule n!*(n+1) = (n+1)! applies even when n=0.
For p(k), break the problem into two parts. First, how many different sequences of n steps are there? Are they equally likely? What is the probability of each sequence? Next, how many sequences consist of k steps one way and n-k the other?
 
  • #5
haruspex said:
You may also be under the misapprehension that 0! = 0. By convention, 0!=1. This makes sense because it means that the rule n!*(n+1) = (n+1)! applies even when n=0.

So, 0! = 1. Still, 1! = 1, right?
Also, about:

haruspex said:
For p(k), break the problem into two parts. First, how many different sequences of n steps are there? Are they equally likely? What is the probability of each sequence? Next, how many sequences consist of k steps one way and n-k the other?

Since the different sequences of n steps there are are 2n, where x + n, x -n both have probability value of 1/2n, whilst the other possibilities are somewhere between two extreme distances, making up of the probability value of (2n -1)/2n.
As for k = (x + n)/2 after the boy's initial position has moved n steps...the max value k can have is when n steps were in the west direction, as in x - n = 2k -n, in this case, x = 2k...then...because k is dependent on n values, if k = n, then x = 2(n) - n = n...so, how do I get from here? Or, am I getting lost?
 
  • #6
terp.asessed said:
So, 0! = 1. Still, 1! = 1, right?
Also, about:
Since the different sequences of n steps there are are 2n, where x + n, x -n both have probability value of 1/2n, whilst the other possibilities are somewhere between two extreme distances, making up of the probability value of (2n -1)/2n.
As for k = (x + n)/2 after the boy's initial position has moved n steps...the max value k can have is when n steps were in the west direction, as in x - n = 2k -n, in this case, x = 2k...then...because k is dependent on n values, if k = n, then x = 2(n) - n = n...so, how do I get from here? Or, am I getting lost?

I cannot figure out what you are asking, but before you wanted to know why ##P(k) = \binom{n}{k}/2^n##. Is that still what is bothering you? Well, look at some small-scale examples, where you can enumerate all the possibilities. Let the successive outcomes (steps) be labelled as R or L, depending on their direction.

For n = 3 the possible outcomes are RRR, RRL, RLR, RLL, LLL, LLR, LRL, LRR. There are 8 different outcomes, each having probability 1/8 = 1/2^3. We have P(3R,0L) = P(RRR) = 1/8, P(2R,1L) = P(RRL)+P(RLR) + P(LRR) = 3 * 1/8, P(1R,2L) = P(RLL) + P(LRL) + P(LLR) = 3 * 1/8, and P(0R,3L) = P(LLL) = 1/8. So, the coefficients C(3,k) just count the number of different ways of having k Rs and (3-k) Ls.

In general, for k Rs and n-k Ls, we have many different "strings", such as RR...RLL...L, RRLR...LLL LR, etc. It is not too difficult to show that the total number of distinct such strings is
[tex] \text{no. of strings } \; = \binom{n}{k} = \frac{n!}{k! (n-k)!} = \frac{n(n-1) \cdots (n-k+1)}{k!} [/tex]
In general, if ##p## is the probability of a step to the right and ##q = 1-p## is the probability of a step to the left, the probability of ##k## stepes to the right in ##n## steps altogether is
[tex] P(k) = \binom{n}{k} p^k q^{n-k}[/tex]
In the special case ##p = q = 1/2## one gets ##P(k) = \binom{n}{k}/2^n##.
 
  • Like
Likes terp.asessed
  • #7
Thank you very much.

One last clarification...
Ray Vickson said:
In (c): the value of ##\langle X^2 \rangle## looks at the average of all the possible ##x^2## values. Only one of the very many outcomes has all steps to the right (or all to the left). The contribution of those two extreme outcomes to ##\langle X^2 \rangle## would be ##2 C(n,0) n^2 / 2^n = 2 n^2/2^n## and so is small compared with the total due to other outcomes.

I think I made a mistake somewhere, for I got <x2> = n, not n2...what I did was:

<k2> = sigma (k=0 to n) k2 p(k) = (1/2n) sigma k2 (n,k) = 1/(2n) n(n+1)2n-2 = n(n+1)/4
<k> as done previously = n/2
<x2> = <(2k-n)2> = 4<k2> - 4<k>n + n2 = n2 + n - 2n2 + n2 = n

...so, I got C(n,0) n = 2 (1/2n n!/n!) = 2n/2n, NOT 2n2/2n...

Could you please point out where I did wrong? If so, merci beaucoup!
 
  • Like
Likes Ray Vickson
  • #8
##\langle X^2 \rangle = n## is correct.

I cannot figure out what your "C(n,0)n" business is all about, or why you should care. The fact is that C(n,0) = 1 so C(n.0)n = n. End of story.
 
  • #9
Got it!
 

Related to Probability: having trouble with explanations

1. What is probability?

Probability is a measure of the likelihood that a certain event will occur. It is often expressed as a number between 0 and 1, where 0 represents impossibility and 1 represents certainty.

2. How is probability calculated?

The probability of an event is calculated by dividing the number of favorable outcomes by the total number of possible outcomes. This is known as the classical definition of probability.

3. Can you give an example of probability in real life?

An example of probability in real life is flipping a coin. The probability of getting heads is 1/2 or 0.5, as there are two possible outcomes (heads or tails) and only one of them is favorable.

4. What is the difference between theoretical and experimental probability?

Theoretical probability is based on mathematical calculations and assumes that all outcomes are equally likely. Experimental probability is based on actual data and observations from experiments or real-life events.

5. How can I use probability in decision making?

Probability can be used in decision making by providing information about the likelihood of different outcomes. This can help individuals or businesses make more informed choices and minimize risks.

Similar threads

  • Precalculus Mathematics Homework Help
Replies
11
Views
378
  • Precalculus Mathematics Homework Help
Replies
3
Views
717
  • Precalculus Mathematics Homework Help
Replies
15
Views
1K
  • Precalculus Mathematics Homework Help
Replies
10
Views
1K
  • Precalculus Mathematics Homework Help
Replies
2
Views
1K
  • Precalculus Mathematics Homework Help
Replies
8
Views
1K
  • Precalculus Mathematics Homework Help
Replies
14
Views
688
  • Precalculus Mathematics Homework Help
Replies
3
Views
1K
  • Precalculus Mathematics Homework Help
Replies
13
Views
1K
  • Precalculus Mathematics Homework Help
Replies
12
Views
2K
Back
Top