8. A Gaussian random variable x with a mean and variance of ax and Ox? respectively...
The input to a system is a Gaussian random variable below X with zero mean and variance of σ- as shown x System The output of the system is a random variable Y given as follows: -a b, X>a (a) Determine the probability density function of the output Y (b) Now assume that the following random variable is an input to the system at time t: where the amplitude A is a constant and phase s uniformly distributed over (0,2T)....
blem 4 , The input to a system is a Gaussian random variable below X with zero mean and variance of σ as shown System The output of the system is a random variable Y given as follows: bX (a) Determine the probability density function of the output Y b) Now assume that the following random variable is an input to the system at time t: where the amplitude A is a constant and phase θ is uniformly distributed over...
1. The random variable X is Gaussian with mean 3 and variance 4; that is X ~ N(3,4). $x() = veze sve [5] (a) Find P(-1 < X < 5), the probability that X is between -1 and 5 (inclusive). Write your answer in terms of the 0 () function. [5] (b) Find P(X2 – 3 < 6). Write your answer in terms of the 0 () function. [5] (c) We know from class that the random variable Y =...
X is a Gaussian random variable with zero mean and variance ơ2 This random variable 5 20 points is passed through a quantizer device whose input-output relation is g(z) = Zn, for an x < an+1, 1 N where In lies in the interval [an, Qn+1) and the sequence fa, a2, al z-00, aN41 # oo, and for i > j we have ai > aj. Find the PMF of the output random variable Y g(X). aN+1) satisfies the conditions
5. A random variable X ∼ N (µ, σ2 ) is Gaussian distributed with mean µ and variance σ 2 . Given that for any a, b ∈ R, we have that Y = aX + b is also Gaussian, find a, b such that Y ∼ N (0, 1) Please show your work. Thanks!
A stochastic process X() is defined by where A is a Gaussian-distributed random variable of zero mean and variance σ·The process Xt) is applied to an ideal integrator, producing the output YO)X(r) dr a. Determine the probability density function of the output Y) at a particular time t b. Determine whether or not Y) is strictly stationary Continuing with Problem 4.3, detemine whether or not the integrator output YC) produced in response to the input process Xit) is ergodic. A...
Problem1 Let Y=aX + b . (a) Find the mean and variance of Y in terms of the mean and variance of X (b) Evaluate the mean and variance ofYifXhas the following PDF (c) Evaluate the mean and variance of Y if Xis the Gaussian random variable with mean 0 and variance of 1 d) Evaluate the mean and variance of Yif X bcos(2RU) where U is a uniform random variable in the unit interval. Problem1 Let Y=aX + b...
5. [20 points] X is a Gaussian random variable with zero mean and variance σ2. This random variable is passed through a hard-limiter device whose input-output relation is b r <0 Find the PDF of the output random variable Yg(X)
Suppose that X is a Gaussian Random Variable with zero mean and unit variance. Let Y=aX3 + b, a > 0 Determine and plot the PDF of Y
Let ˜x and ˜y be zero-mean, unit variance Gaussian random variables with correlation coefficients, . Suppose we form two new random variables using linear transformations: Let and be zero-mean, unit variance Gaussian random variables with correlation coefficients, p. Suppose we form two new random variables using linear transformations: Find constraints on the constants a, b, e, and d such that ù and o are inde- pendent.