Probability- Exponential Random Variables

In summary, the conversation discusses the use of large deviation methodology to find a lower bound for the rate function R(a) when X1, X2... are independent and identically distributed mean 1 exponential random variables. The equation R(a) \leq \frac{-logP[Sn >n*a]}{n} is mentioned, and the attempt at a solution involves finding the probability using the pdf of a Gamma distribution, but the integral is difficult to solve. Help is requested for proceeding with finding the limit as n tends to infinity for R(a).
  • #1
Roni1985
201
0

Homework Statement



Suppose X1,X2... are iid mean 1 exponential random variables. Use large deviation methodology to give a lower bound for the rate function R(a) for a>1

Homework Equations



R(a) [tex]\leq[/tex] [tex]\frac{-logP[Sn >n*a]}{n}[/tex]

The Attempt at a Solution



I know that a sum of exponential random variables is Gamma (n, 1).

I'm having a problem with finding the probability.

the pdf of a gamma dist random variable is:[tex]\frac{x^(^n^-^1^)*e^(^-^x^)}{(n-1)!}[/tex]
I think after transforming it, this is the pdf.

But I'm getting a very hard integral and even my TI-89 can't solve it.
I think I'm doing something wrong.
 
Last edited:
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  • #2
I know that the rate function is the limit as n tends to infinity of R(a) so I don't know how to proceed.Any help would be greatly appreciated!
 

1) What is an exponential random variable?

An exponential random variable is a type of continuous random variable that represents the time between events in a Poisson process. It is often used to model the time between occurrences of rare events.

2) How is an exponential random variable different from other types of random variables?

Unlike other types of random variables, an exponential random variable has a constant rate of change over time. This means that the probability of an event occurring at any given time is independent of all previous events.

3) What is the probability density function for an exponential random variable?

The probability density function (PDF) for an exponential random variable is given by f(x) = λe^(-λx), where λ is the rate parameter and x is the time. This function describes the probability of an event occurring at a specific time.

4) How is the mean of an exponential random variable calculated?

The mean of an exponential random variable is equal to 1/λ, where λ is the rate parameter. This means that as the rate parameter increases, the mean decreases, and vice versa.

5) Can an exponential random variable have a negative value?

No, an exponential random variable cannot have a negative value. This is because it represents the time between events, and time cannot be negative. The range of an exponential random variable is from 0 to infinity.

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