Is the Memoryless Property of Exponentials Affected by an Upper Bound?

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In summary, the memoryless property of exponentials does not fully apply when conditioned on an upper bound, but it still applies in part. This means that the expected value of a random variable X, given that it falls within a certain range, can be calculated by adding 2 to the expected value of a new exponential random variable Y that is conditioned on being less than the upper bound of the range.
  • #1
hellokitten
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Homework Statement


Let x be an exponential random variable with lamda = .5

P(x=5|2<x<9} = 0 since exponential is continuous => probability of any single number = 0
Calculate E[x| 2<x<9] = integral from 2 to 9 of (x* .5*exp(-.5*x)) dx ? is this true or is there something wrong because of the memoryless property of exponentials?

The Attempt at a Solution

 
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  • #2
hellokitten said:
Calculate E[x| 2<x<9] = integral from 2 to 9 of (x* .5*exp(-.5*x)) dx ? is this true or is there something wrong because of the memoryless property of exponentials?
No, that equation is not quite right. Imagine reducing the given range to a very narrow one. The expected value should then be some value in that range, but your integral will tend to zero. What have you forgotten?

I don't understand why you think it would violate the memorylessness though. The conditional is not saying anything about the outcome of a previous trial; it's giving you information about the outcome of the present trial.
 
  • #3
P(5|2<x<9) often is referring to the PDF evaluated at 5 divided by the (cdf at 9 - cdf at 2)
The expected value integral looks okay to me.
 
  • #4
"Give that 2< x< 9" means that the probability that x is beween 2 and 9 is 1. The "conditional probability" that "[itex]a\le x\le b[/itex]' where, of course, [itex]2< a< b< 9[/itex], is [tex]\frac{\int_a^b e^{-.5t}dt}{\int_2^9 e^{-.5t}dt}[/tex]
 
  • #5
RUber said:
The expected value integral looks okay to me.
Yes, the integral is ok in itself, but
RUber said:
... divided by the (cdf at 9 - cdf at 2)
Exactly - the division is missing.
 
  • #6
hellokitten said:

Homework Statement


Let x be an exponential random variable with lamda = .5

P(x=5|2<x<9} = 0 since exponential is continuous => probability of any single number = 0
Calculate E[x| 2<x<9] = integral from 2 to 9 of (x* .5*exp(-.5*x)) dx ? is this true or is there something wrong because of the memoryless property of exponentials?

The Attempt at a Solution


hellokitten said:

Homework Statement


Let x be an exponential random variable with lamda = .5

P(x=5|2<x<9} = 0 since exponential is continuous => probability of any single number = 0
Calculate E[x| 2<x<9] = integral from 2 to 9 of (x* .5*exp(-.5*x)) dx ? is this true or is there something wrong because of the memoryless property of exponentials?

The Attempt at a Solution


Because of the upper bound ##X < 9## the memoryless property does not apply in full. However, it applies in part. Conditioned on ##X > 2##, the (conditional) distribution of ##X## is the same as that of ##2+Y##, where ##Y \sim \text{Expl}(\lambda = 0.5)##. We can thus write
[tex] E(X | 2 < X < 9) = 2 + E(Y|Y < 7) = 2 + E(X | X < 7)[/tex]
Whether or not you think this is useful is for you to decide.
 
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FAQ: Is the Memoryless Property of Exponentials Affected by an Upper Bound?

What is an Expotential distribution?

An Expotential distribution is a probability distribution that describes the time between events in a Poisson process, which is a process in which events occur continuously and independently at a constant average rate. It is often used to model the time between arrivals or failures of systems.

What are the key characteristics of an Expotential distribution?

The key characteristics of an Expotential distribution include:

  • It is a continuous probability distribution
  • The values are always positive
  • The distribution is skewed to the right
  • The mean and standard deviation are equal
  • The shape of the distribution is determined by a single parameter, the rate parameter (λ)

How is an Expotential distribution different from a Normal distribution?

An Expotential distribution and a Normal distribution are different in several ways:

  • An Expotential distribution is a continuous probability distribution, while a Normal distribution is a continuous and symmetric probability distribution.
  • The values in an Expotential distribution are always positive, while a Normal distribution can have negative values.
  • The shape of an Expotential distribution is skewed to the right, while a Normal distribution is symmetric.
  • An Expotential distribution is often used to model the time between events, while a Normal distribution is often used to model continuous data.

What is the relationship between the mean and the standard deviation in an Expotential distribution?

In an Expotential distribution, the mean and the standard deviation are equal and are both equal to the inverse of the rate parameter (λ). This means that as the rate parameter increases, the mean and standard deviation decrease, and as the rate parameter decreases, the mean and standard deviation increase.

How is an Expotential distribution used in real-world applications?

An Expotential distribution is commonly used in real-world applications to model the time between events, such as the time between customer arrivals at a store or the time between failures of a machine. It is also used in reliability analysis to estimate the reliability of systems based on the distribution of time between failures.

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