Simplification step by step (exponential and logarithm) - regarding BLOOM FILTER - FALSE POSITIVE pr

antennense

antennense

Answered question

2022-07-11

Simplification step by step (exponential and logarithm) - regarding BLOOM FILTER - FALSE POSITIVE probability
( 1 1 m ) k n e k n / m
I don't get how to reach e k n / m from ( 1 1 m ) k n
I have reviewed logarithm and exponent rules but I always get stuck, here is what I have tried; Starting from ( 1 1 m ) k n , I can write:
- e ( l n ( 1 1 m ) k n )
- e ( k n × l n ( 1 1 m ) )
Or the other way around:
l n ( e ( 1 1 m ) k n )
l n ( e ( 1 1 m ) × k n )
l n ( e ( 1 1 m ) ) + l n ( k n )
( 1 1 m ) + l n ( k n )
After few steps I fall in an unsolvable loop hole playing with l n and e trying to reach e k n / m .

Answer & Explanation

Mateo Carson

Mateo Carson

Beginner2022-07-12Added 15 answers

Knowing that
(1) ( 1 1 m ) m e 1
it's as simple as
(2) ( 1 1 m ) k n = ( ( 1 1 m ) m ) k n / m ( e 1 ) k n / m = e k n / m
The first formula is a simple variation of ( 1 + 1 m ) m e, which is a well known limit.
As pointed out in a comment, this derivation does not specify how the parameters n, m, k, should be related for the asymptotics to hold. A simple requirement would be that m and k n / m tends to some positive constant. But this might be unrealistic. A better derivation would use
( 1 1 m ) m = e 1 ( 1 + O ( 1 / m ) )
hence
( 1 1 m ) k n = e k n / m ( 1 + O ( 1 / m ) ) k n / m
This shows that, for the approximation to hold, it is sufficient that m 1 and k n m 2 1, or, equivalently, that
m 2 k n .

Do you have a similar question?

Recalculate according to your conditions!

New Questions in College Statistics

Ask your question.
Get an expert answer.

Let our experts help you. Answer in as fast as 15 minutes.

Didn't find what you were looking for?