1. A method, comprising: determining by a computing device a plurality of trained Gaussian processes related to signal strengths of wireless networks, wherein a particular trained Gaussian process in the plurality of trained Gaussian processes is associated with one or more hyperparameters; determining by the computing device one or more designated hyperparameters of the one or more hyperparameters; determining by the computing device a hyperparameter histogram of a plurality of values for the one or more designated hyperparameters, wherein one or more particular values in the plurality of values are one or more values for the one or more designated hyperparameters associated with a trained Gaussian process of the plurality of trained Gaussian processes, and wherein the hyperparameter histogram comprises a plurality of histogram bins; after determining by the computing device the hyperparameter histogram, determining a candidate Gaussian process by the computing device, wherein the candidate Gaussian process is associated with one or more candidate hyperparameter values for the one or more designated hyperparameters, and wherein the one or more candidate hyperparameter values are associated with a candidate histogram bin of the plurality of histogram bins; determining by the computing device whether the one or more candidate hyperparameter values are valid based on the hyperparameter histogram by: determining one or more mean values and one or more standard deviation values for the values of the one or more designated hyperparameters represented by the hyperparameter histogram, determining whether the candidate histogram bin is an outlier histogram bin based on the one or more mean values and the one or more standard deviation values, and after determining that the candidate histogram bin is not an outlier histogram bin, determining that the one or more candidate hyperparameter values are valid; after determining by the computing device that the one or more candidate hyperparameter values are valid, adding by the computing device the candidate Gaussian process to the plurality of trained Gaussian processes; receiving a request related to locating a mobile device at the computing device; determining by the computing device an estimate of the location of the mobile device based on the plurality of trained Gaussian processes; generating by the computing device an estimated location output that comprises the estimate of the location of the mobile device; and providing the estimated location output using the computing device.