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myProcess= AggregatedProcess([WhiteNoise(Normal(mu = 0, sigma = 1))])
a realization= [ t X0 ]
0 : [ 0 0.608202 ]
1 : [ 0.1 -1.26617 ]
2 : [ 0.2 -0.438266 ]
3 : [ 0.3 1.20548 ]
4 : [ 0.4 -2.18139 ]
5 : [ 0.5 0.350042 ]
6 : [ 0.6 -0.355007 ]
7 : [ 0.7 1.43725 ]
8 : [ 0.8 0.810668 ]
9 : [ 0.9 0.793156 ]
10 : [ 1 -0.470526 ]
a marginal process= WhiteNoise(Normal(mu = 0, sigma = 1))
myProcess= AggregatedProcess([WhiteNoise(Normal(mu = 0, sigma = 1)),ARMA(X_{0,t} = E_{0,t}, E_t ~ Normal(mu = 0, sigma = 1)),GaussianProcess(trend=[x0]->[0], covariance=ExponentialModel(scale=[1], amplitude=[1], no spatial correlation))])
a realization= [ t X0 X0 y0 ]
0 : [ 0 -1.28289 0.351418 -0.0436123 ]
1 : [ 0.1 -1.31178 1.78236 0.190168 ]
2 : [ 0.2 -0.0907838 0.0702074 0.299777 ]
3 : [ 0.3 0.995793 -0.781366 0.444838 ]
4 : [ 0.4 -0.139453 -0.721533 0.195966 ]
5 : [ 0.5 -0.560206 -0.241223 0.0142559 ]
6 : [ 0.6 0.44549 -1.78796 -0.307618 ]
7 : [ 0.7 0.322925 0.40136 -0.16853 ]
8 : [ 0.8 0.445785 1.36783 0.685721 ]
9 : [ 0.9 -1.03808 1.00434 0.33466 ]
10 : [ 1 -0.856712 0.741548 1.09293 ]
a marginal process= ARMA(X_{0,t} = E_{0,t}, E_t ~ Normal(mu = 0, sigma = 1))
another marginal process= AggregatedProcess([WhiteNoise(Normal(mu = 0, sigma = 1)),GaussianProcess(trend=[x0]->[0], covariance=ExponentialModel(scale=[1], amplitude=[1], no spatial correlation))])
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