5.2 Square law detector - continued. Continue to consider Example 5.2, in which Y = g(x) = x2 (a) Let X have a uniform...
5. Let X have a uniform distribution on the interval (0,1). Given X = x, let Y have a uniform distribution on (0, 2). (a) The conditional pdf of Y, given that X = x, is fyıx(ylx) = 1 for 0 < y < x, since Y|X ~U(0, X). Show that the mean of this (conditional) distribution is E(Y|X) = , and hence, show that Ex{E(Y|X)} = i. (Hint: what is the mean of ?) (b) Noting that fr\x(y|x) =...
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,...
3-5.2. Let X, Y, and Z have the joint pdf 3/2 1 |ryz exp exp 27T 2 where -o<x < o,-00< y < oo, and 00< z < 00. While X, Y, and Z are obviously dependent, show that X, Y, and Z are pairwise independent and that each pair has a bivariate normal distribution 3-5.2. Let X, Y, and Z have the joint pdf 3/2 1 |ryz exp exp 27T 2 where -o
1. Consider the uniform distribution X defined over the interval [0, 2pi]. Now let Y = sin(X) (a) Calculate the CDF FY(y) of Y. (b) Calculate the PDF f(y) of Y. In particular, in what interval [a, b] is Y defined? (this mean f(y) = 0 for y < a and for y > b). (c) Verify that f(y) is a PDF.
5. Let X have the uniform distribution U(0, 1), and let the conditional distribution of Y, given X = x, be U(0, x). Find P(X + Y ≥ 1).
Let (X,Y) have joint density f(x,y) -2x for0 <x < 1,0sys1 and 0 elsewhere. (a) Find P(xY > z) for 0szs1. Your final answer should be a function of z. (Hint: if you pick up a particular z, say,武what is the area within the unit square of 0 x 1 and 0 y 1 such that xy > z? P1.68 shows what you need to do, i.e., a double integral. Note thatz is a constant from the perspective of both...
We start out with a couple of defintions and examples. Definition: Let X and Y have joint pdf f(x,y). The conditional pdf of Y given X = x (resp. of X given Y = y) is defined by h(y|x) = f (x, y) resp. g(x|y) = f (x, y) f1(x) f2(y) If A is a subset of the real line, then P(Y ∈A|X =x)= h(y|x)dy resp. P(X ∈A|Y =y)= g(x|y)dx . AA Example 1 (seen in class) Consider the joint...
Let (X, Y) have joint density and 0 elsewhere. (a) Find P(XY > z) for 0 ss z up a particular z, say, what is the area within the unit square of 0 x 1 and 0 y 1 such that xyz? P1.68 shows what you need to do, i.e., a double integral. Note that z is a constant from the perspective of both x and y.) Find the cumulative distribution function of the random variable Z ะ-XY. Your final...
Consider a cylindrical capacitor like that shown in Fig. 24.6. Let d = rb − ra be the spacing between the inner and outer conductors. (a) Let the radii of the two conductors be only slightly different, so that d << ra. Show that the result derived in Example 24.4 (Section 24.1) for the capacitance of a cylindrical capacitor then reduces to Eq. (24.2), the equation for the capacitance of a parallel-plate capacitor, with A being the surface area of...