Solving Random Walk Equations

In summary, we want to show that the number of permutations satisfying \sum_{j\displaystyle{=}1}^n jx_{j} \leq a is equal to the number of permutations satisfying \sum_{j\displaystyle{=}1}^n jx_{j} \geq \frac{n(n+1)^2}{2}-a . To prove this, we show that given a permutation (x_{1},x_{2},...,x_{n}) satisfying \sum_{j\displaystyle{=}1}^n jx_{j} \leq a , we can use this permutation to find another permutation (y_{1},y_{2
  • #1
dirk_mec1
761
13

Homework Statement


http://img171.imageshack.us/img171/4711/52047019pc2.png

Homework Statement





Homework Equations


Random walk equations: Xi is i.i.d. and [tex]S_n = X_1+...+X_n[/tex] and S0=0


The Attempt at a Solution


I don't have a idea where to begin. I couldn't start because I don't know how to apply the random walk.
 
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  • #2
For starters, there may be a slight error in the question. I think the result to be proven should be [tex] P\displaystyle{\{} \sum_{j\displaystyle{=}1}^n jX_{j} \leq a \displaystyle{\}} = P\displaystyle{\{} \sum_{j\displaystyle{=}1}^n jX_{j} \geq \frac{n(n+1)^2}{2}-a \displaystyle{\}} [/tex].

Since [tex](X_{1},X_{2},...,X_{n})[/tex] is equally likely to be any of the n! permutations of (1,2,...,n), it suffices to prove that the number of permutations

satisfying [tex] \sum_{j\displaystyle{=}1}^n jx_{j} \leq a [/tex] is equal to the number of permutations satisfying [tex] \sum_{j\displaystyle{=}1}^n jx_{j} \geq \frac{n(n+1)^2}{2}-a [/tex].

Now, to prove this, we show that given a permutation [tex] (x_{1},x_{2},...,x_{n}) [/tex] satisfying [tex] \sum_{j\displaystyle{=}1}^n jx_{j} \leq a [/tex], we can use this permutation to find another permutation [tex] (y_{1},y_{2},...,y_{n}) [/tex] having the property [tex] \sum_{j\displaystyle{=}1}^n jy_{j} \geq \frac{n(n+1)^2}{2}-a [/tex], and vice versa.

How can the other permutation be found? Hint: In a permutation, order matters! So, moving the terms around seems a good place to start.
 
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  • #3
Yes, you're right about the mistake, my instructor told us that but I forgot to tell you.So by permutations they mean:

[tex] \left( \begin{array}{c}n \\ x \end{array} \right) [/tex]

where x can be 1,...,n and the answer for one x corresponds to a Xi, right?But then [tex] \sum_{j=1}^n j x_j = \sum_{j=1}^n \left( \begin{array}{c}n \\ x \end{array} \right) = \sum_{j=1}^n \left( \begin{array}{c}n \\ n-x \end{array} \right)[/tex]

Is this what you mean? But how can I proceed then because if the above expression is smaller than a how can I pop up a n(n+1)^2?
 
  • #4
A permutation is simply an arrangement of the numbers 1, 2, 3, ..., n. So, examples of permutations are (1, 2, 3, ..., n-1, n) ,
(2, 3, 4, ..., n, 1) etc. There are n! such permutations in all, as mentioned in the question.
 
  • #5
pizzasky said:
A permutation is simply an arrangement of the numbers 1, 2, 3, ..., n. So, examples of permutations are (1, 2, 3, ..., n-1, n) ,
(2, 3, 4, ..., n, 1) etc. There are n! such permutations in all, as mentioned in the question.

Right, I understand. But how can I interpretet then (for example) [tex] \sum_{j=1}^n j X_j = 1 \cdot (1, 2, 3, ..., n-1, n) + 2\cdot (2, 3, 4, ..., n, 1)+... [/tex]

I presume the sum means 'per element of the permutation' but how can such a thing be bounded by a? Is that componentwise bounded?
 
  • #6
Take for example the permutation [tex] (x_{1},x_{2},...,x_{n}) = (1, 2, ..., n) [/tex]. We have [tex] \sum_{j=1}^n j x_j = 1^2 + 2^2 + ... + n^2 [/tex].

Thus, the question is asking us to show that the probability of a permutation [tex] (x_{1},x_{2},...,x_{n}) [/tex] having the property [tex] \sum_{j\displaystyle{=}1}^n jx_{j} \leq a [/tex] is the same as the probability of a permutation having the property [tex] \sum_{j\displaystyle{=}1}^n jx_{j} \geq \frac{n(n+1)^2}{2}-a [/tex].
 
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  • #7
I've thought about your hint and I don't see how changing the order is going to effect anything.

Suppose x= (x1,..xn) =(1,...,n) then if you calculate the sum you'll get a sum of squares:

[tex] 1^2 +2^2+...+n^2 = \frac{n(n+1)(2n+1)}{ 6} [/tex]

Is this the direction where I should be going?
 
  • #8
I believe changing the order of the terms is going to have an effect on the sum at least. Suppose [tex] (x_{1},x_{2},...,x_{n}) = (1, 2, ..., n)[/tex] and [tex] (y_{1},y_{2},...,y_{n}) = (n, ..., 1)[/tex]. In general, [tex] \sum_{j\displaystyle{=}1}^n jx_{j}[/tex] is not equal to [tex] \sum_{j\displaystyle{=}1}^n jy_{j} [/tex].

Here's a possible approach to the question:

Let A be the set of permutations [tex] (x_{1},x_{2},...,x_{n}) [/tex] satisfying [tex] \sum_{j\displaystyle{=}1}^n jx_{j} \leq a [/tex] and B be the set of permutations [tex] (y_{1},y_{2},...,y_{n}) [/tex] satisfying [tex] \sum_{j\displaystyle{=}1}^n jy_{j} \geq \frac{n(n+1)^2}{2}-a [/tex]. Let |A| and |B| denote the number of elements in A and B respectively. Our goal is to show that |A| = |B| (refer to the second post). We will prove this statement in 2 parts.


Part 1: [tex] |A| \leq |B| [/tex]
We prove this by showing that for every permutation [tex] (x_{1},x_{2},...,x_{n}) [/tex] in A, we can change the order of the terms in a certain way to create a permutation [tex] (y_{1},y_{2},...,y_{n}) [/tex] that will be in B. This will mean that |B| cannot be smaller than |A|, and thus [tex] |A| \leq |B| [/tex] (since this mapping from set A to set B is injective or one-to-one). Now, consider a permutation [tex] (x_{1},x_{2},...,x_{n}) [/tex] in A. We will try to shuffle the terms around in a certain manner, and show that the new permutation [tex] (y_{1},y_{2},...,y_{n}) [/tex] we get is an element of B. What is one obvious guess for [tex] (y_{1},y_{2},...,y_{n}) [/tex]? Try then to show that [tex] \sum_{j\displaystyle{=}1}^n jy_{j} \geq \frac{n(n+1)^2}{2}-a [/tex], given the fact that [tex] \sum_{j\displaystyle{=}1}^n jx_{j} \leq a [/tex].

Part 2: [tex] |B| \leq |A| [/tex]
The idea of this part is the same as before; we show that for every permutation [tex] (y_{1},y_{2},...,y_{n}) [/tex] in B, we can change the order of the terms in a certain way to create a permutation [tex] (x_{1},x_{2},...,x_{n}) [/tex] that will be in A. The structure of the proof is similar to Part 1.

Combining our results for both parts, we get |A| = |B| and we are done.
 
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1. What is a random walk equation?

A random walk equation is a mathematical model that describes the path of a random process, where each step is determined by a random variable. It is often used to analyze the behavior of systems that involve randomness, such as stock prices, particle movements, or biological processes.

2. How do you solve a random walk equation?

To solve a random walk equation, you need to first define the initial conditions and the rules for how each step is determined. Then, using probability theory and mathematical techniques such as Markov chains, you can calculate the probabilities of different outcomes and the behavior of the system over time.

3. What are some real-world applications of solving random walk equations?

Random walk equations have many practical applications, including predicting stock market trends, modeling the spread of diseases, analyzing the movements of particles in a liquid, and simulating the behavior of animals in their natural habitats. They are also used in computer science for generating random numbers and in cryptography for creating secure encryption algorithms.

4. What are the limitations of using random walk equations?

One of the main limitations of random walk equations is that they assume that the system being modeled is completely random and does not take into account external factors or influences. This can lead to inaccurate predictions in real-world scenarios where there may be other variables at play. Additionally, random walk equations can become computationally complex and difficult to solve for systems with a large number of variables.

5. Can random walk equations be applied to non-linear systems?

Yes, random walk equations can be applied to non-linear systems, but they may require more complex mathematical techniques to solve. In some cases, linear approximations can be used to simplify the equations and make them more manageable. However, for highly non-linear systems, other modeling approaches may be more suitable.

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