Abstract: Processing a measurement includes receiving a first set of at least one measurement. The first set of at least one measurement is processed to generate conditional information corresponding to the first set of at least one measurement. The processing includes: generating a plurality of possible samples, each possible sample representing a sample from a prior probability distribution, mapping at least each measurement in the first set, and each possible sample, according to a nonlinear mapping procedure, into corresponding vectors in a target space, and generating the conditional information according to an error probability density function that is based at least in part on differences between the mapped vectors in the target space.

Abstract: A computer-implemented method of generating, with respect to a surface, a model-specific, computationally efficient representation of a data set pertaining to a property of the surface. In one embodiment, the data set is mapped with an index function to create mapped data points; the mapped data points are grouped based on a value of the index function for each of the mapped data points; using a selection function, mapped points are selected, for each group, for inclusion in a final representation; and a final representation of the data set is generated using, for each mapped point in each group which is selected for inclusion, a corresponding original data point. The method is capable of generating a variable resolution model by using a physics-based or model-specific index function to indicate the impact of data on a model.