A geometric random variable is the count of Bernouli trial untill a success. we count the probability of obtaining n−1 failures and then 1 success.
PX(n) = p (1−p)n-1 :n=1,2,…
The sum of two such is the count of Bernouli trials untill the 2nd success. the probability of getting 1 success and (n−2) failures, in any arrangement of those (n−1) trials, followed by the second success. is given by:
Px+y(n)=(n−1)(1−p)n−2p2 n=2,3….........
Now
due to independenc of X and Y
which is the required pmf of x+y. where n=2,3,4,........
Problem 41.3 Let X and Y be independent random variables each geometrically distributed with parameter p,...
Let X and Y be independent random variables which are exponential with parameter lambda= 1, so then each has probability density function equal to f(x) = exp(-x) when x > 0, and zero otherwise. Compute the probability density function of X + Y . Show detailed explanations and reasoning for each step.
8. Let the random variables X be the sum of independent Poisson distributed random variables, i.e., X = -1 Xi, where Xi is Poisson distributed with mean 1. (a) Find the moment generating function of Xi. (b) Derive the moment generating function of X. (d) Hence, find the probability mass function of X.
8. Let the random variables X be the sum of independent Poisson distributed random variables, i.e., X = 11-1Xị, where Xi is Poisson distributed with mean li. (a) Find the moment generating function of Xį. (b) Derive the moment generating function of X. (d) Hence, find the probability mass function of X.
Problem 42.5 Let X and Y be two independent and identically distributed random variables with common density function f(x) 2x 0〈x〈1 0 otherwise Find the probability density function of X Y. 42.5 If 0 < a < l then ÍxHY(a) 2a3. If 1 < a < 2 then ÍxHY(a) -릎a3 + 4a-3. If a 〉 2 then fx+y(a) 0 and 0 otherwise.
Problem 4. Let X and Y be independent Geom(p) random variables. Let V - min(X, Y) and Find the joint mass function of (V, W) and show that V and W are independent
rate parameter A, for y independent rate parameter A, for X,. Let Y be the minimum of all these n random variables, i.e., Y- min(X1, X2,... ,Xn). Show that Y is distributed as exponential with rate Problem 6. Let X1, X2,..., Xn be independent exponential random variables with rn.
Let X and Y be two independent standard nor- mally distributed random variables, i.e., both X and Y follows standard normal function (each has mean zero and variance one). we define the random variable Z := X^2 + Y ^2. Compute Z’s density function for all real values (should be exponential with some parameter).
3. Suppose that X and Y are independent exponentially distributed random variables with parameter λ, and further suppose that U is a uniformly distributed random variable between 0 and 1 that is independent from X and Y. Calculate Pr(X<U< Y) and estimate numerically (based on a visual plot, for example) the value of λ that maximizes this probability.
Suppose that X and Y are independent, identically distributed, geometric random variables with parameter p. Show that P(X = i|X + Y = n) = 1/(n-1) , for i = 1,2,...,n-1
Let X and Y be two independent and identically distributed random variables with expected value 1 and variance 2.56. First, find a non-trivial upper bound for P(|X + Y − 2| ≥ 1). Now suppose that X and Y are independent and identically distributed N(1,2.56) random variables. What is P(|X + Y − 2| ≥ 1) exactly? Why is the upper bound first obtained so different from the exact probability obtained?