Question

Let Y denote a geometric random variable with probability of success p, (a) Show that for...

Let Y denote a geometric random variable with probability of success p, (a) Show that for a positive integer a, P(Y > a) = (1 − p) a (b) Show that for positive integers a and b, P(Y > a + b|Y > a) = P(Y > b) = (1 − p) b This is known as the memoryless property of the geometric distribution.

Homework Answers

Know the answer?
Your Answer:

Post as a guest

Your Name:

What's your source?

Earn Coins

Coins can be redeemed for fabulous gifts.

Not the answer you're looking for?
Ask your own homework help question
Similar Questions
Let B~Binomial(n,p) denote a binomially distributed random variable with n trials and probability of success p....
Let B~Binomial(n,p) denote a binomially distributed random variable with n trials and probability of success p. Show that B / n is a consistent estimator for p.
(14pts) Let X and Y be i.i.d. geometric random variables with parameter (probability of success) p,...
(14pts) Let X and Y be i.i.d. geometric random variables with parameter (probability of success) p, 0 < p < 1. (a) (6pts) Find P(X > Y ). (b) (8pts) Find P(X + Y = n) and P(X = k∣X + Y = n), for n = 2, 3, ..., and k = 1, 2, ..., n − 1.
Let X be a discrete random variable with positive integer outputs a show that p (X=...
Let X be a discrete random variable with positive integer outputs a show that p (X= K)= P( X> K-1) - P( X> k) for any positive integer k b Assume that for all k >I we have P (X>k)=q^k  use l() to show that X is a geometric random variable
let x be a discrete random variable with positive integer outputs. show that P(x=k) = P(...
let x be a discrete random variable with positive integer outputs. show that P(x=k) = P( x> k-1)- P( X>k) for any positive integer k. assume that for all k>=1 we have P(x>k)=q^k. use (a) to show that x is a geometric random variable.
Let X denote a random variable that follows a binomial distribution with parameters n=5, p=0.3, and...
Let X denote a random variable that follows a binomial distribution with parameters n=5, p=0.3, and Y denote a random variable that has a Poisson distribution with parameter λ = 6. Additionally, assume that X and Y are independent random variables. Derive the joint probability distribution function for X and Y. Make sure to explain your steps.
How to use Chebyshev bound to achieve this question ? Let X be a Geometric random...
How to use Chebyshev bound to achieve this question ? Let X be a Geometric random variable, with success probability p. 1) Use the Markov bound to find an upper bound for P (X ≥ a), for a positive integer a. 2) If p = 0.1, use the Chebyshev bound to find an upper bound for P(X ≤ 1). Compare it with the actual value of P (X ≤ 1) which you can calculate using the PMF of Geometric random...
Let X1 and X2 be two independent geometric random variables with the probability of success 0...
Let X1 and X2 be two independent geometric random variables with the probability of success 0 < p < 1. Find the joint probability mass function of (Y1, Y2) with its support, where Y1 = X1 + X2 and Y2 = X2.
Let X and Y be independent Geometric(p) random variables. (a) What is P(X < Y)? (b)...
Let X and Y be independent Geometric(p) random variables. (a) What is P(X < Y)? (b) What is the probability mass function of the minimum min(X, Y )?
Let X be a geometric random variable with parameter p . Find the probability that X≥10...
Let X be a geometric random variable with parameter p . Find the probability that X≥10 . Express your answer in terms of p using standard notation (click on the “STANDARD NOTATION" button below.)
Let X denote a random variable that follows a binomial distribution with parameters n=5, p=0.3, and...
Let X denote a random variable that follows a binomial distribution with parameters n=5, p=0.3, and Y denote a random variable that has a Poisson distribution with parameter λ = 6. Additionally, assume that X and Y are independent random variables. Using the joint pdf function of X and Y, set up the summation /integration (whichever is relevant) that gives the expected value for X, and COMPUTE its value.