Exercise 3. We say that a family of random variables (X)teo.) converges in probability to a random variable X (notation...
Exercise 3-6.1 Two random variables X and Y have a joint probability density function of the form 148 CHAPTER 3 SEVERAL RANDOM VARIABLES -0 elsewhere Find the probability density function of Z-XY. Answer: -In (z) Exercise 3-6.2 Show that the random variables X and Y in Exercise 3-6.1 are independent and find the expected value of their product. Find ElZ] by integrating the function zf(z) Answer: 1/4
Exercise about two-dimensional random variables, independence and covariation: Suppose, two-dimensional random variable (X, Y) has probability density function as follows: 0y1 + f(x, y) 2xy) ,0 <x<1, otherwise 0 Find c Find marginal probability density functions of X and Y-find f(x) and f(y) and find if X and Y are independent; Find joint (X, Y) distribution function; Find covariation of X and Y find Cov(X, Y) and correlation p(X, Y). What can be concluded? Suppose, two-dimensional random variable (X, Y)...
Recall that a discrete random variable X has Poisson distribution with parameter λ if the probability mass function of X Recall that a discrete random variable X has Poisson distribution with parameter λ if the probability mass function of X is r E 0,1,2,...) This distribution is often used to model the number of events which will occur in a given time span, given that λ such events occur on average a) Prove by direct computation that the mean of...
8. We say that two discrete random variables X and Y , are independent when P(X = a, Y = b) = P(X = a)P(Y = b) for all a and b in the corresponding sample spaces. Let Xị and X, be independent Poisson random variables with parameters l1 = 3 and dy = 2 respectively. Find the probability of the event that X1 + X2 = 3. Hint: Since {X1 + X2 = 3} = {X} = 0, X2...
A Random Telegraph Signal with rate λ > 0 is a random process X(t) (where for each t, X(t) ∈ {±1}) defined on [0,∞) with the following properties: X(0) = ±1 with probability 0.5 each, and X(t) switches between the two values ±1 at the points of arrival of a Poisson process with rate λ i.e., the probability of k changes in a time interval of length T isP(k sign changes in an interval of length T) = e −λT...
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.
Question 3 Suppose that the random variable X has the Poisson distribution, with P (X0) 0.4. (a) Calculate the probability P (X <3) (b) Calculate the probability P (X-0| X <3) (c) Prove that Y X+1 does not have the Polsson distribution, by calculating P (Y0) Question 4 The random variable X is uniformly distributed on the interval (0, 2) and Y is exponentially distrib- uted with parameter λ (expected value 1 /2). Find the value of λ such that...
1. (10) Suppose the random variables X and Y have the joint probability density function 4x 2y f(x, y) for 0 x<3 and 0 < y < x +1 75 a) Determine the marginal probability density function of X. (6 pts) b) Determine the conditional probability of Y given X = 1. (4 pts)
3.5. Suppose that X and Tare independent, continuous random variables and that U-X+1. Denote their probability density functions by f(x), g(y) and h(u) and the corresponding cumulative probability functions by F(x), G(2) and H(u) respectively. Then For a fixed value of I, say T-y,this probability is F(u-), and the probability that I will lie in the range y to y+dy is g()dy. Hence the probability that Usu and that simultaneously Y lies between y and y+dy is F(u-)go)dy and so...
3. Let the random variables X and Y have the joint probability density function fxr (x, y) = 0 <y<1, 0<xsy otherwise (a) Compute the joint expectation E(XY). (b) Compute the marginal expectations E(X) and E(Y). (c) Compute the covariance Cov(X,Y).