Let X have a geometric distribution with f(x)=p(1-p)x, x=0,1,2,… Find the probability function of R, the remainder when X is divided by 4.

Sandra Terrell

Sandra Terrell

Answered question

2022-08-06

Finding the Probability function of the remainder.
Let X have a geometric distribution with f ( x ) = p ( 1 p ) x, x = 0 , 1 , 2 , . Find the probability function of R, the remainder when X is divided by 4.
I am stuck on the above practice question: If I say R = 0 , 1 / 4 , 2 / 4 , … and then use the continuous case equivalent of geometric distribution to find the probability function of R, which is exponential distribution, will I be correct?

Answer & Explanation

Dominique Mayer

Dominique Mayer

Beginner2022-08-07Added 10 answers

Step 1
I'm not sure what you mean by "using the continuous case equivalent of geometric distribution to find the probability function of R". However, here's how I would approach the problem:
When you divide X by R, there are three possible remainders: 0,1,2, and 3. You will get a remainder of 0 for multiples of 4, that is, when X = 0 , 4 , 8 , . You will get a remainder of 1 for numbers one more than the multiples of 4, such as 1,5,9,…. Hopefully, you get the idea by now.
With that in mind, the probabilities are as follows: clearly, P ( R = r ) = 0 for all numbers r besides 0,1,2,3. For 0,1,2,3, we have:
P ( R = 0 ) = p ( 1 p ) 0 + p ( 1 p ) 4 + p ( 1 p ) 8 + = k = 0 p ( 1 p ) 4 k P ( R = 1 ) = p ( 1 p ) 1 + p ( 1 p ) 5 + p ( 1 p ) 9 + = k = 0 p ( 1 p ) 4 k + 1 P ( R = 2 ) = p ( 1 p ) 2 + p ( 1 p ) 6 + p ( 1 p ) 1 0 + = k = 0 p ( 1 p ) 4 k + 2 P ( R = 3 ) = p ( 1 p ) 3 + p ( 1 p ) 7 + p ( 1 p ) 1 1 + = k = 0 p ( 1 p ) 4 k + 3
Step 2
From there, you could use the formula for the sum of a geometric series. Alternatively, you could simply make the following observations:
P ( R = 3 ) = ( 1 p ) P ( R = 2 ) P ( R = 2 ) = ( 1 p ) P ( R = 1 ) P ( R = 1 ) = ( 1 p ) P ( R = 0 ) P ( R = 0 ) + P ( R = 1 ) + P ( R = 2 ) + P ( R = 3 ) = 1
And solve the above system of equations to find the required probabilities.

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