Measuring the performance of sensors that report uncertainty

2014/11/17 by A. D. Martin, Martin, A. D., T. C. A. Molteno +3
Physics and Astronomy · #Data Analysis #FOS: Physical sciences #Instrumentation and Detectors (physics.ins-det) #Statistics and Probability (physics.data-an) #physics.data-an #physics.ins-det

paper · pdf · doi:10.48550/arxiv.1411.4354

5 pages, 4 figures. Accepted for proceedings of the Electronics New Zealand Conference 2014

arxiv created 2014/11/17 · arxiv updated 2015/07/07

Abstract

We provide methods to validate and compare sensor outputs, or inference algorithms applied to sensor data, by adapting statistical scoring rules. The reported output should either be in the form of a prediction interval or of a parameter estimate with corresponding uncertainty. Using knowledge of the `true' parameter values, scoring rules provide a method of ranking different sensors or algorithms for accuracy and precision. As an example, we apply the scoring rules to the inferred masses of cattle from ground force data and draw conclusions on which rules are most meaningful and in which way.

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