Data Driven Evaluation and Rejection of Trained Gaussian Process-Based Wireless Mean and Standard Deviation Models

A computing device, comprising:

• one or more processors;
• and
• data storage, configured to store at least computer-readable program instructions, wherein the instructions are configured to, upon execution by the one or more processors, cause the computing device to perform functions comprising:
• determining 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 one or more designated hyperparameters of the one or more hyperparameters;
• determining a hyperparameter histogram of a plurality of values of 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;
• after determining the hyperparameter histogram, determining a candidate Gaussian process, wherein the candidate Gaussian process is associated with one or more candidate hyperparameter values for the one or more designated hyperparameters;
• determining whether the one or more candidate hyperparameter values are valid based on the hyperparameter histogram;
• after determining that the one or more candidate hyperparameter values are valid, adding the candidate Gaussian process to the plurality of trained Gaussian processes;
• and
• providing an estimated location output based on the plurality of trained Gaussian processes.

An article of manufacture including a computer-readable storage medium having instructions stored thereon that, in response to execution by one or more processors, cause the one or more processors to perform functions comprising:

• determining 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 one or more designated hyperparameters of the one or more hyperparameters;
• determining a hyperparameter histogram of a plurality of values for the one or more designated hyperparameters using the computing device, 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;
• after determining the hyperparameter histogram, determining a candidate Gaussian process, wherein the candidate Gaussian process is associated with one or more candidate hyperparameter values for the one or more designated hyperparameters;
• determining whether the one or more candidate hyperparameter values are valid based on the hyperparameter histogram;
• after determining that the one or more candidate hyperparameter values are valid, adding the candidate Gaussian process to the plurality of trained Gaussian processes;
• and
• providing an estimated location output based on the plurality of trained Gaussian processes.