| 摘要 |
Described herein is a framework for analyzing data in high-dimensional space. In accordance with one implementation, observed data and at least one input model parameter set is received. The input model parameter set serves as a solution candidate of a predefined problem (e.g., inverse or optimization problem) and is related to the observed data via a model. To provide enhanced computational efficiency, a reduced base with lower dimensionality is determined based on the input model parameter set. The reduced base is associated with a set of coefficients, which represents the coordinates of any model parameter set in the reduced base. Sampling is performed within the reduced base to generate an output model parameter set in the reduced base. The output model parameter set is compatible with the input model parameter set and fits the observed data, via the model, within a predetermined threshold. |
| 主权项 |
1. A method of determining uncertainty in high-dimensional space, comprising:
(i) receiving, by a processor, observed data from a data source and at least one input model parameter set serving as a solution candidate of a predefined problem, wherein the input model parameter set is related to the observed data via a model; (ii) determining based on the input model parameter set, by the processor, a reduced base associated with a set of coefficients that represent coordinates of any model parameter set in the reduced base, wherein the coefficients in the reduced base are fewer than model parameters in the input model parameter set; and (iii) sampling within the reduced base, by the processor, to generate an output model parameter set in the reduced base, wherein the output model parameter set is compatible with the input model parameter set and fits the observed data, via the model, within a predetermined threshold; and (iv) generating, by the processor, one or more uncertainty measures based on the output model parameter set. |
