Probability - random variables, poisson/binomial distributions

In summary, we are trying to estimate the probability of a 1000-character text file being transferred with no errors when each character has a 0.001 probability of being corrupted. Using the Poisson random variable, we can approximate the probability by using the parameter np, which gives a value of e^-1. This is in contrast to using the binomial random variable, where the probability can be found by plugging in n=1000 and p=0.001, resulting in a value of approximately 0.37.
  • #1
Kate2010
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Homework Statement


A text file contains 1000 characters. When the file is sent by email from one machine to another, each character (independent of other characters) has probability 0.001 of being corrupted. Use a poisson random variable to estimate the probability that the file is transferred with no errors. Compare this to the answer you get when modelling the number of errors as a binomial random variable.


Homework Equations


p_x(k) = (a^k)(e^-a)/(k!), poisson
p_x(k) = (n choose k)(p^k)(q^{n-k}), binomial


The Attempt at a Solution


Poisson:
Let x be the number of corrupted characters.
E(X) = 0.001 = a
P(X=n) = (0.001^n)(e^-0.001)/(n!)
P(X=0) = e^-0.001

Binomial:
E(x) = np = 1000 x 0.001 = 1

I don't really think I'm tackling either of these problems in the correct way but don't know what else to do. Thanks for any help.
 
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  • #2
I don't think you have to worry about expected values here. When you model the number of errors with a binomial distribution, you want to find the probability of 0 "successes." "Success" in this case means a character being corrupted, which happens with probability .001. So you want to find p(k=0) using the formula

[tex] p(k)={n \choose k} p^k (1-p)^{n-k} [/tex].

For the poisson distribution, notice here that n is large and p small. When this is the case, a binomial distribution can be approximated by poission with parameter [tex]\lambda =np.[/tex]
 
  • #3
I'm still a bit confused, when I use poisson I get P(X=n) = (0.001^n)(e^-0.001)/(n!), so P(X=0) is e^-0.001 which is 0.99999 recurring. However, when I try it as you suggested, again using poisson but with np instead I get e^-1 which is about 0.37.
 
  • #4
Kate2010 said:
I'm still a bit confused, when I use poisson I get P(X=n) = (0.001^n)(e^-0.001)/(n!), so P(X=0) is e^-0.001 which is 0.99999 recurring. However, when I try it as you suggested, again using poisson but with np instead I get e^-1 which is about 0.37.

Apologies for the (very) late response.

When you write P(X=n) = (0.001^n)(e^-0.001)/(n!), you are saying that the Poisson parameter is .001, which is not true. In this problem, the Poisson parameter is supposed to be approximated by np, as mentioned above.

The value .001 is used for modeling the number of errors as a binomial random variable, that is, the probability of no "successes" (i.e. no characters corrupted) is

[tex] P(X=0)=p(0)={n\choose 0} .001^0 (1-.001)^{n-0} [/tex]

where n=1000, the number of trials.
 
  • #5
Thanks, I understand now :)
 

FAQ: Probability - random variables, poisson/binomial distributions

What is a random variable?

A random variable is a variable that can take on different numerical values based on the outcome of a random event. It is often represented by the letter "X".

What is the difference between a discrete and continuous random variable?

A discrete random variable can only take on a finite or countably infinite number of values, while a continuous random variable can take on any value within a certain range.

What is the Poisson distribution?

The Poisson distribution is a probability distribution that is used to model the number of events that occur in a certain time or space interval. It is often used in situations where events occur randomly and independently of each other.

What is the Binomial distribution?

The Binomial distribution is a probability distribution that is used to model the number of successes in a fixed number of independent trials. It is often used in situations where there are only two possible outcomes for each trial (e.g. success or failure).

How do you calculate the mean and variance of a random variable?

The mean of a random variable is calculated by multiplying each possible value of the variable by its corresponding probability and then summing these products. The variance is calculated by taking the square of the difference between each possible value and the mean, multiplying it by its corresponding probability, and then summing these products.

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