METHOD AND SYSTEM FOR MULTIPLE DATASET GAUSSIAN PROCESS MODELING
First Claim
1. A method of computerised data analysis and synthesis comprising receiving first and second datasets relating to a quantity of interest within a domain, each dataset comprising a plurality of datapoints, storing the first and second datasets, generating a Gaussian process model by using the first and second datasets to compute optimized kernel and noise hyperparameters, storing the optimized kernel and noise hyperparameters, and applying the Gaussian process model by 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 within the domain wherein generating a Gaussian process model comprises using the first dataset to learn optimized kernel hyperparameters and a first noise hyperparameter, and using the learnt kernel hyperparameters and second dataset to learn an optimized second noise hyperparameter, and wherein Gaussian process regression is performed using the first and second datasets, first and second noise hyperparameters and kernel hyperparameters.
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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.
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Citations
34 Claims
- 1. A method of computerised data analysis and synthesis comprising receiving first and second datasets relating to a quantity of interest within a domain, each dataset comprising a plurality of datapoints, storing the first and second datasets, generating a Gaussian process model by using the first and second datasets to compute optimized kernel and noise hyperparameters, storing the optimized kernel and noise hyperparameters, and applying the Gaussian process model by 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 within the domain wherein generating a Gaussian process model comprises using the first dataset to learn optimized kernel hyperparameters and a first noise hyperparameter, and using the learnt kernel hyperparameters and second dataset to learn an optimized second noise hyperparameter, and wherein Gaussian process regression is performed using the first and second datasets, first and second noise hyperparameters and kernel hyperparameters.
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4. (canceled)
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17. A system for analysing and synthesising data to estimate a quantity of interest, comprising:
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a training processor adapted to obtain and store first and second datasets, each comprising a plurality of datapoints, representing separate sets of measurements or indicators of the quantity of interest within a domain, generate a Gaussian process model by using, the first dataset to learn optimized kernel hyperparameters and a first noise hyperparameter, and the learnt kernel hyperparameters and the second dataset to learn a second noise hyperparameter; data storage in communication with the training processor, wherein the training processor is adapted to store in the data storage the first and second datasets and computed hyperparameters, and an evaluation processor in communication with the data storage, the evaluation processor adapted to apply the Gaussian process model by performing Gaussian process regression using both the first and second datasets, respective first and second noise hyperparameters, and kernel hyperparameters to compute estimates of unknown values of the quantity of interest within the domain. - View Dependent Claims (18, 19, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32)
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21. (canceled)
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33. (canceled)
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34. (canceled)
Specification