Abstract: A method for modelling a dataset includes a training phase, wherein the dataset is applied to a non-stationary Gaussian process kernel in order to optimize the values of a set of hyperparameters associated with the Gaussian process kernel, and an evaluation phase in which the dataset and Gaussian process kernel with optimized hyperparameters are used to generate model data. The evaluation phase includes a nearest neighbour selection step. The method may include generating a model at a selected resolution.

Abstract: A method for modelling a dataset includes a training phase, wherein the dataset is applied to a non-stationary Gaussian process kernel in order to optimize the values of a set of hyperparameters associated with the Gaussian process kernel, and an evaluation phase in which the dataset and Gaussian process kernel with optimized hyperparameters are used to generate model data. The evaluation phase includes a nearest neighbor selection step. The method may include generating a model at a selected resolution.

Abstract: A method of computerized data analysis and synthesis is described. First and second datasets of a quantity of interest are stored. A Gaussian process model is generated using the first and second datasets to compute optimized kernel and noise hyperparameters. The Gaussian process model is applied using the stored first and second datasets and hyperparameters to perform Gaussian process regression to compute estimates of unknown values of the quantity of interest. The resulting computed estimates of the quantity of interest result from a non-parametric Gaussian process fusion of the first and second measurement datasets. The first and second datasets may be derived from the same or different measurement sensors. Different sensors may have different noise and/or other characteristics.

Abstract: A method of computerised data analysis and synthesis is described. First and second datasets of a quantity of interest are stored. A Gaussian process model is generated using the first and second datasets to compute optimized kernel and noise hyperparameters. The Gaussian process model is applied using the stored first and second datasets and hyperparameters to perform Gaussian process regression to compute estimates of unknown values of the quantity of interest. The resulting computed estimates of the quantity of interest result from a non-parametric Gaussian process fusion of the first and second measurement datasets. The first and second datasets may be derived from the same or different measurement sensors. Different sensors may have different noise and/or other characteristics.

Abstract: Methods, systems, and computer program products for generating capacity planning schedules while protecting the privacy of stakeholder preferences of a set of metrics are provided herein. A computer-implemented method includes identifying stakeholders associated with capacity planning for a project; determining metrics to be used in the capacity planning; obtaining, from each of the stakeholders, an initial preferred order of emphasis of the metrics; calculating similarity scores between the initial preferred orders of emphasis; outputting, to each of the stakeholders, the similarity scores, wherein the identity of the stakeholders has been masked; obtaining, from each of the stakeholders, at least a second iteration of a preferred order of emphasis of the metrics; generating a final order of emphasis of the multiple metrics upon a determination that the stakeholders provided at least a predetermined number of identical preferred orders of emphasis; and outputting the final order of emphasis of the metrics.