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, in a data storage, the first and second datasets; generating, using a training processor, a Gaussian process model by using the first and second datasets to compute optimized kernel and noise hyperparameters; storing, in the data storage, the optimized kernel and noise hyperparameters; and using an evaluation processor, 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 the Gaussian process model includes, in the training processor: 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.