Use-case 1 (Code in a <pre> tag) ==> __TODO__ # seed X_0 = 0 X <- 0 # purely random process with mean 0 and standard deviation 1.5 Z <- rnorm(100, mean = 0.5, sd = 1.5) # the process for (i in 2:length(Z)){ X[i] <- X[i-1] + Z[i] } # process plotting ts.plot(X, main […]

Adding Equations P(Xt1≤x1,Xt2≤x2,…,Xtk≤xk)=F(xt1,xt2,…,xtk)=F(xh+t1,xh+t2,…,xh+tk)=P(Xh+t−1≤x1,Xh+t2≤x2,…,Xh+tk≤xk) Xt A stationary process {X−t,t∈N} is said to be strictly or strongly stationary if its statistical distributions remain unchanged after a shift o the time scale. Since the distributions of a stochastic process are defined by the finite-dimensional distribution functions, we can formulate an alternative definition of strict stationarity. If in […]

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