asap plz The joint probability density function of random variables X and Y is given by,...
Random variables \(X\) and \(Y\) have joint probability mass function (PMF):\(P_{X, Y}\left(x_{k}, y_{j}\right)=P\left(X=x_{k}, Y=y_{j}\right)= \begin{cases}\frac{1}{20}\left|x_{k}+y_{j}\right|, & x_{k}=-1,0,1 ; y_{j}=-3,0,3 \\ 0, & \text { otherwise }\end{cases}\)(a) Find \(F_{X, Y}(x, y)\), the joint cumulative distribution function (CDF) of \(X\) and \(Y\). A graphical representation is sufficient: probably the best way to do this is to draw the \(x-y\) plane and label different regions with the value of \(F_{X, Y}(x, y)\) in that region.(b) Let \(Z=X^{2}+Y^{2}\). Find the probability mass function (PMF)...
Two random variables are jointly distributed with joint pdf given by: = 0, elsewhere a) Find the value of K? b) Find the best LMMSE of Y. what is the MMSE error in this case? c) Find the best MMSE estimator of Y? d) What is minimum mean square error of Y given that x -1 Two random variables are jointly distributed with joint pdf given by: = 0, elsewhere a) Find the value of K? b) Find the best...
1. (Hint: This pmf should look familiar) Random variables X and Y have joint probability mass function (IPMI): otherwise. (a) Find Fx,y(x, y), the joint cumulative distribution function (CDF) of X and Y. A graphical repre- sentation is sufficient: probably the best way to do this is to draw the x - y plane and label different regions with the value of Fx,y(x, y) in that region. (b) Let Z = X2 + Y2. Find the probability mass function (PMF)...
Given that the random variables X and Y have joint probability density function =J24.ry, ar > 0, y>0,x+1, < 1; otherwise, f(r, y) , find the regression curve of Y on X
The joint probability density function of two continuous random variables X and Y is Find the value of c and the correlation of X and Y. Consider the same two random variables X and Y in problem [1] with the same joint probability density function. Find the mean value of Y when X<1. fxy(x,y) = { C, 0 <y < 2.y < x < 4-y 10, otherwise
Section 6.5: Mean Square Estimation 6.68. Let X and Y be discrete random variables with three possible joint pmf's: Let X and Y have joint pdf: fx.y(x, y) -k(x + y) for 0 sxs 1,0s ys1 Find the minimum mean square error linear estimator for Y given X. Find the minimum mean square error estimator for Y given X. Find the MAP and ML estimators for Y given X. Compare the mean square error of the estimators in parts a,...
2. Let X and Y be continuous random variables with joint probability density function fx,y(x,y) 0, otherwise (a) Compute the value of k that will make f(x, y) a legitimate joint probability density function. Use f(x.y) with that value of k as the joint probability density function of X, Y in parts (b),(c).(d),(e (b) Find the probability density functions of X and Y. (c) Find the expected values of X, Y and XY (d) Compute the covariance Cov(X,Y) of X...
The joint probability density function for continuous random variables X and Y is given below. f (x) = x + y, 0 < x < 1, 0 < y < 1 if; 0, degilse. (a) Show that this is a joint density function. (b) Find the marginal density of X . (c) Find the marginal density of Y . (d) Given Y = y find the conditional density of X . (e) P ( 1/2 < X < 1|Y =...
The joint probability density function for continuous random variables X and Y is given below. f (x) = x + y, 0 < x < 1, 0 < y < 1 if; 0, degilse. (a) Show that this is a joint density function. (b) Find the marginal density of X . (c) Find the marginal density of Y . (d) Given Y = y find the conditional density of X . (e) P ( 1/2 < X < 1|Y =...
The joint probability density function for continuous random variables X and Y is given below. f (x) = x + y, 0 < x < 1, 0 < y < 1 if; 0, degilse. (a) Show that this is a joint density function. (b) Find the marginal density of X . (c) Find the marginal density of Y . (d) Given Y = y find the conditional density of X . (e) P ( 1/2 < X < 1|Y =...