Given a WSS Gaussian random process X(t,), t, =1, 2, 3, the autocorrelation function is Ryy (T) =16e1 with mean X=3. Find the covariance matrix.
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- Find X2 (the probability distribution of the system after two observations) for the distribution vector X0 and the transition matrix T. X0 = 0.9 0.1 , T = 0.1 0.6 0.9 0.4 X2 =Suppose Xn is an IID Gaussian process, withµX[n]=1, and σ2 X[n]=1Now, another stochastic process Yn = Xn − Xn−1. Please find:(a) The mean µY (n).(b) The variance σ2Y (n).(c) The auto-correlation RY (n, k)why is the covariance of a deterministic and a stochastic process 0? This relats to Arithmetic Bronian Motion
- Let the stochastic process {Xt} be defined as Zt ; if t is even (Z2t-1 -1)=21/2; if t is uneven, where {Zt} is identically and independently distributed as Zt is N(0, 1). Show that {Xt} is WN(0, 1), but not IID (0,1).Consider the time series xt = β1 + β2t + wt, where β1 and β2 are known constants and wt is a white noise process with variance σ2 w. (a) Determine whether xt is stationary. (b) Show that the process yt = xt − xt−1 is stationary. (c) Show that the mean of the moving average vt = 1 2q + 1 q j=−q xt−j is β1 + β2t, and give a simplified expression for the autocovariance function.The joint probability mass function p(x,y) of X and Y is defined as follows: p(0,0)=0.5 p(1,0)=0.2 p(0,1)=0.2 p(1,1)=0.1 a) What is the covariance between X and Y? b) What is the correlation coefficient of X and Y? c) Are X and Y are dependent? What about are X and Y are uncorrelated?
- If Kt = B2t - t, where B is standard Brownian Motion, show that Kt is a martingale, and a markov processLet X, Y, and Z be random variables, and let Cov(⋅,⋅) denote the covariance operator as usual. Suppose that the variance of X is 0.7, Cov(X,Y) = 0.4, Cov(X,Z) = 1.2, and Cov(Y,Z) = 0.8. Find each of the following to two decimal places. (a) Cov(3Y, 3X) (b) Cov(3Y + 3, 3X + 8Z)Let A be an n × n positive stochastic matrix withdominant eigenvalue λ1 = 1 and linearly independenteigenvectors x1, x2, . . . , xn, and let y0 be aninitial probability vector for a Markov chainy0, y1= Ay0, y2= Ay1, . . . Show that if y0 = c1x1 + c2x2 +· · ·+cnxn then the component c1 in the direction of the positive eigenvector x1 must be nonzero.
- Define the random process {Xt} by Xt = et + θ et−1. Show this process is weakly stationary.For any continuous random variables X, Y , Z and any constants a, b, show the following from the definition of the covariance:Suppose that X0, X1, X2, ... form a Markov chain on the state space {1, 2}. Assume that P(X0 = 1) = P(X0 = 2) = 1/2 and that the matrix of transition probabilities for the chain has the following entries: Q11 = 1/2, Q12 = 1/2, Q21 = 1/3, Q22 = 2/3. Find limn→∞ P(Xn = 1).