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In the classical theory [124] one-dimensional zero-mean Gaussian noise n(t) is a one-parameter family of real-valued Gaussian random variables such that for every sequence 0 = t0 < t1 < t2 < ⋯ < tk, the vector
where xT = (x1, x2, … , xk). If the noise is uncorrelated, that is, if n(ti) is independent of n(tj) for i≠j, then σ is a diagonal matrix with σi, i = Var[n(ti)]. This case is easy to simulate on a computer, because \(n({t}_{i}) \backsim \mathcal{N}(0,{\sigma }^{i,i})\), that is, in a Monte–Carlo simulation we sample the random component n(ti) of the vector n, independently of all others, from the normal distribution \(\mathcal{N}(0,{\sigma }^{i,i})\). …
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