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In this way, a covariance function is learned implicitly from the data, and the computational complexity is reduced compared to GPs, while the theoretical advantages of Bayesian modeling are preserved. Donner Apr Abstract: In this thesis, we explore the problem of constructing valid covariance matrices of Gaussian Processes defined on river networks, with the aim to predict river discharges at different locations and time points along the river stream. However, the performance of the fourth and fifth experiments are more promising. While the maximum entropy bootstrap is the method that is by far the most successful at creating time series that have the desired features, it lacks diversity in its output as it is very strongly correlated to the original series, which makes it unsuitable for some tasks in practice.