Generating Function for Probability of Equal Sums in Multinomial Distributions?

In summary, the conversation discusses a problem of finding the probability of purchasing a ticket with a number whose sums of the first three and last three digits are equal. The solution involves using a generating function, which is the product of the probability generating functions of the six digits on the ticket. The solution also involves using basic knowledge of numerical sequences to find the number of possible arrangements for each sum. Finally, the probability is calculated by summing up the products of the number of arrangements for each sum.
  • #1
JierenChen
11
0
I don't understand the generating function used to find the probability that the sum of the numbers of occurring events is a certain quantity. Specifically, I'm having trouble with this problem:

"Find the probability of purchasing a ticket with a number whose sums of the first three and last three digits are equal if it has six digits and may be any number from 000000 to 999999"
 
Physics news on Phys.org
  • #2
What do you not understand? You do not know how to solve the question? Or you do not know something about multinomial distribution?

I may have some ideas about how to solve the question, but are not usre that will work (or that is correct). First you got six digits on a ticket. The six digits are *independently identically distributed* with a discrete uniform distibution from 0 to 9. Call the digits [tex] z_{i} [/tex], i from 1 to 6. The probability generating function (pgf)of [tex] z_{i} [/tex] is [tex] \frac{1-t^{10}}{9(1-t)} [/tex]. Now the pgf of [tex] z_{1}+z_{2}+z_{3} = \frac{(1-t^{10})^3}{27(1-t)^3} [/tex] (because [tex] z_{1}, z_{2}, z_{3}[/tex] are independent). Now find the pgf of [tex] -z_{4}-z_{5}-z_{6} [/tex]. How may one find it? The pgf of [tex] -z_{i} [/tex] is [tex] \frac{1-\frac{1}{t^{10}}}{9(1-\frac{1}{t})} [/tex] and so the pgf of [tex] -z_{4}-z_{5}-z_{6} [/tex] is the third power of [tex] \frac{1-\frac{1}{t^{10}}}{9(1-\frac{1}{t})} [/tex].

Now the problem is to find the probability of the sum of the first three digits being equal the sum of the last three. This is equivalent to [tex] Pr(z_{1}+z_{2}+z_{3}-z_{4}-z_{5}-z_{6}=0) [/tex]. The pgf of the random variable [tex]z_{1}+z_{2}+z_{3}-z_{4}-z_{5}-z_{6}[/tex] is just the product of that of [tex] z_{1}+z_{2}+z_{3}[/tex] and that of [tex]-z_{4}-z_{5}-z_{6} [/tex] (as they are all independent). Now you may just "read off" the probability from the pgf.

Not sure the solution is correct. But still I hope that may help.
 
  • #3
"Find the probability of purchasing a ticket with a number whose sums of the first three and last three digits are equal if it has six digits and may be any number from 000000 to 999999"

I found an interesting solution,somewhat 'mechanical' (based on 'empirical observations') but without losing any generality,which involves also some basic knowledge of numerical sequences.Here it is.

If the digits of the ticket number are z1 z2 z3 z4 z5 z6 then we can have:

z1+z2+z3=n= 0 OR 1 OR 2 OR 3...OR 27 (1) and

z4+z5+z6=n= 0 OR 1 OR 2 OR 3...OR 27 respectively (2)

(the condition is that z1+z2+z3=z4+z5+z6)

If z1+z2+z3=n=0 then we have a single possibility to arrange the digits in z1 z2 z3,namely 000.

If z1+z2+z3=n=1 ---> 001,010,100 ---> 3 ways

If z1+z2+z3=n=2 ---> 011,101,110,002,020,200 ---> 6 ways

If z1+z2+z3=n=3 ---> 111,021,012,120,102,210,201,003,030,300 ---> 10 ways

If z1+z2+z3=n=4 ---> 004,040,400,013,031,103,130,301,310,112,121,211,122,202,220 ---> 15 ways

The interesting fact is that for z1+z2+z3=n=0,1...,13 the number of ways in which we can write z1 z2 z3 is given by a linear sequence.

Indeed (as must be observed from above,easily to verify numerically,though cumbersome):

n=0 ---> a[n]=1=the possible ways to arrange the digits in z1 z2 z3

n=1 ---> a[1]=3

n=2 ---> a[2]=6

n=3 ---> a[3]=10

n=4 ---> a[4]=15
.
.
.
n=13 ---> a[13]=105

The closed term for a[n] is:

a[n]=(1/2)*n2+(3/2)n+1

The situation is symmetric for n=14,...27 by observing that for n=14 we are exactly in the situation of n=13,n=15 --> n=12,...n=27 ---> n=0.

Exactly the same considerations can be applied to z4 z5 z6.


Now for n=0 for example we have:

z1 z2 z3 ---> 1 way of arranging the digits

z4 z5 z6 ---> 1 way of arranging the digits

Thus the number of ways in which we can write z1 z2 z3 z4 z5 z6 with the condition that z1+z2+z3=z4+z5+z6=0 is N[0]=1*1=1 way

For n=1 we have:

z1 z2 z3 ---> 3 ways of arranging the digits

z4 z5 z6 ---> 3 ways of arranging the digits

The number of ways in which we can write z1 z2 z3 z4 z5 z6 with the condition that z1+z2+z3=z4+z5+z6=1 is N[1]=3*3=9 ways.

Analogously for n=2 ---> N[2]=6*6=36 and so on till n=13.

By observing that for n=14,...,27 we have symmetry we can compute the required probability P as:

P=∑ from k=0 to k=13 2*{[(1/2)*k2+(3/2)k+1]2}/1000000
 
Last edited:
  • #4
Thanks all for your replies. I'm starting to understand the problem but I'm still not exactly sure what generating functions are supposed to do.

coudl anyone help me?
 
  • #5
Chen, so your problem is that you do not understand what are the uses of a generating function?
 
  • #6
Actually now I understand. I'm just now sure exactly how to get to the generating function.
 

What is a multinomial distribution?

A multinomial distribution is a probability distribution that describes the outcomes of a categorical variable with more than two possible outcomes. It is a generalization of the binomial distribution, which describes the outcomes of a categorical variable with only two possible outcomes.

What are the parameters of a multinomial distribution?

The parameters of a multinomial distribution are the number of trials (n) and the probabilities of each outcome (p1, p2, ..., pk). The sum of the probabilities must equal 1 and the number of probabilities must equal the number of possible outcomes (k).

How is a multinomial distribution different from a binomial distribution?

A multinomial distribution is different from a binomial distribution in that it allows for more than two possible outcomes, while a binomial distribution only has two possible outcomes. Additionally, the probabilities of each outcome in a multinomial distribution do not have to be equal, while in a binomial distribution, the probability of success and failure must be the same.

How is a multinomial distribution used in research?

A multinomial distribution is often used in research to model the probability of multiple categorical outcomes. It can be used in fields such as genetics, economics, and social sciences to analyze data with multiple categories. It can also be used in hypothesis testing to determine if the observed frequencies of outcomes are significantly different from the expected frequencies based on the multinomial distribution.

How is a multinomial distribution related to other probability distributions?

A multinomial distribution is a generalization of the binomial distribution and is also related to other distributions, such as the hypergeometric distribution and the categorical distribution. It can also be approximated by the normal distribution under certain conditions, such as when the number of trials is large and the probabilities are not too close to 0 or 1.

Similar threads

  • Set Theory, Logic, Probability, Statistics
Replies
6
Views
1K
  • Set Theory, Logic, Probability, Statistics
Replies
7
Views
331
  • Set Theory, Logic, Probability, Statistics
Replies
12
Views
2K
  • Set Theory, Logic, Probability, Statistics
Replies
9
Views
406
  • Set Theory, Logic, Probability, Statistics
Replies
8
Views
2K
  • Set Theory, Logic, Probability, Statistics
Replies
6
Views
965
  • Set Theory, Logic, Probability, Statistics
Replies
2
Views
2K
  • Set Theory, Logic, Probability, Statistics
Replies
2
Views
869
  • Set Theory, Logic, Probability, Statistics
Replies
12
Views
2K
  • Quantum Interpretations and Foundations
4
Replies
109
Views
4K
Back
Top