As a general comment, remember that showing two random variables have the same CDFor PDF is...
(10 points) Consider the infinite sequence of independent and identically distributed (stan- dard) uniform random variables: U1, U2, ..., i.e., Ui » Uniform(0,1). Also let N ~ Poisson(a). Assume N is independent of {U;}i>1. Consider the random variable z = į V. Calculate EZ. (Hint: use conditioning.)
Exercise 6.55 Let X and Y be random variables with joint density function f(x, y)- 4 0 otherwise Show that the joint density function of U = 3(X-Y) and V = Y is otherwise, where A is a region of the (u, v) plane to be determined. Deduce that U has the bilateral exponential distribution with density function fu (11) te-lul foru R. Exercise 6.55 Let X and Y be random variables with joint density function f(x, y)- 4 0...
Let T and TR be two independent random variables that have exponential distribution with rates 4 and λ¡R respectively. Find the cdf and pdf for Tr + Ta
(25 points,) Let X and Y be two independent and identically distributed random variables that have exponential distribution with rates 1 respectively. Find the distribution of Note: you can give either cdf or pdf) (25 points,) Let X and Y be two independent and identically distributed random variables that have exponential distribution with rates 1 respectively. Find the distribution of Note: you can give either cdf or pdf)
a) Let X and Y be two random variables with known joint PDF Ir(x, y). Define two new random variables through the transformations W=- Determine the joint pdf fz(, w) of the random variables Z and W in terms of the joint pdf ar (r,y) b) Assume that the random variables X and Y are jointly Gaussian, both are zero mean, both have the same variance ơ2 , and additionally are statistically independent. Use this information to obtain the joint...
Two random variables have joint PDF of F(x, y) = 0 for x < 0 and y < 0 for 0 <x< 1 and 0 <y<1 1. for x > 1 and y> 1 a) Find the joint and marginal pdfs. b) Use F(x, y) and find P(X<0.75, Y> 0.25), P(X<0.75, Y = 0.25), P(X<0.25)
1. Let {y,)%, be a sequence of random variables, and let Y be a random variable on the same sample space. Let A(E) be the event that Y - Y e. It can be shown that a sufficient condition for Y, to converge to Y w.p.1 as n → oo is that for every e0, (a) Let {Xbe independent uniformly distributed random variables on [0, 1] , and let Yn = min (X), , X,). In class, we showed that...
5. Suppose we have two random variables X and Y. They are discrete and have the exact same distribution and also independent. You see below the distribution of X which of course also the distribution of Y as well, that is what we called independent and identically distributed) P(X =- X. Remem- a./ (-) Find and draw the cumulative distribution function F() function of ber that F(x) -P(X S) HINT: For the next 3 parts you might want to make...
Please complete the following problem correctly, showing all steps in computation: Part 4 Two random variables (35 points) {happy, sad and sample space of Y is ssunny, cloudy, rainy} The joint distribution is shown as follows. Is X and Y independent of each other? Show the calculation. (10 points) A. We have two random variables X and Y, both are nominal variables. Sample space of X is Sunny 0.4 Cloudy Rainy 0.05 Happy Sad 0.1 0.1 0.3 0.05
In each problem, make sure that you are clearly defining random variables, stating their distributions, and writing down the formulas that you are using. (That is, write down the pmf, write down mean and variance formulas.) Prove that the exponential distribution is memoryless. That is, let X~Exp(?), and show that for any two positive real numbers x, y, P(X ≥ x + y | X ≥ y) = P(X ≥ x). Hint: Mirror the proof we did in class for...