PMF of Y for Exponential Random Variable

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In summary, for a random variable X with parameter λ, Y = m if m < X < m + 1, the pmf of Y is e^-λm(1-e^-λ). This cannot be classified as binomial or geometric because it does not fulfill the requirements for those distributions. Instead, it is a continuous random variable with a density function.
  • #1
magnifik
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for a random variable X with parameter λ, Y = m if m < X < m + 1
what is pmf of Y?

it's basically asking for P[m< X < m+1]
i know how to solve this for P[m < X < m + 1] ... it would be e-λm - e-λ(m+1) because
P[a < X < b] = Fx(b) - Fx(a) and i know the PDF of an exponential random variable. however, in this problem the inequality does not match that so I'm wondering how to solve. i am unsure how to decompose P[m < X < m + 1]. i don't know of any property of the CDF that matches this
 
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  • #2
magnifik said:
for a random variable X with parameter λ, Y = m if m < X < m + 1
what is pmf of Y?

it's basically asking for P[m< X < m+1]
i know how to solve this for P[m < X < m + 1] ... it would be e-λm - e-λ(m+1) because
P[a < X < b] = Fx(b) - Fx(a) and i know the PDF of an exponential random variable. however, in this problem the inequality does not match that so I'm wondering how to solve. i am unsure how to decompose P[m < X < m + 1]. i don't know of any property of the CDF that matches this

It doesn't matter. For any random variable X that is (absolutely) continuous--that is, which has a density--- the probability of a single point = 0, so if a < b we have P{a <= X <= b} = P{a < X <= b} = P{a <= X < b} = P{a < X < b} = F(b) - F(a).

RGV
 
  • #3
ok. thanks.
 
  • #4
so it would be e-λm - e-λ(m+1) which can be written as
e-λm(1-e) .. would this be binomial or geometric?
 

1. What is the probability mass function (PMF) of a random variable?

The probability mass function (PMF) of a random variable is a function that assigns a probability to each possible value of the random variable. It describes the distribution of the random variable and is used to calculate the probability of obtaining a specific value or a range of values.

2. How is the PMF different from the probability density function (PDF)?

The main difference between the PMF and the PDF is that the PMF is used for discrete random variables, while the PDF is used for continuous random variables. The PMF gives the probability of obtaining a specific value, while the PDF gives the probability of obtaining a value within a certain range.

3. What are the properties of a PMF?

There are three main properties of a PMF: 1) The probability for each possible value of the random variable must be between 0 and 1. 2) The sum of all probabilities must equal 1. 3) The random variable must take on only discrete values.

4. How do you calculate the expected value of a random variable using the PMF?

The expected value of a random variable can be calculated using the PMF by multiplying each possible value by its corresponding probability and summing the results. This can be written as E(X) = ∑ x * p(x), where x is the possible value and p(x) is the probability of obtaining that value.

5. Can the PMF be used to describe both discrete and continuous random variables?

No, the PMF can only be used to describe discrete random variables. For continuous random variables, the probability is described by the probability density function (PDF) instead.

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