Published in Chaos: An Interdisciplinary Journal of Nonlinear Science, Volume 21, Issue 3, July 1, 2011, pages 033102-1-033102-8.
The definitive version is available at https://doi.org/10.1063/1.3593373.
This paper describes a new mathematical method called conflation for consolidating data from independent experiments that measure the same physical quantity. Conflation is easy to calculate and visualize and minimizes the maximum loss in Shannon information in consolidating several independent distributions into a single distribution. A formal mathematical treatment of conflation has recently been published. For the benefit of experimenters wishing to use this technique, in this paper we derive the principal basic properties of conflation in the special case of normally distributed (Gaussian) data. Examples of applications to measurements of the fundamental physical constants and in high energy physics are presented, and the conflation operation is generalized to weighted conflation for cases in which the underlying experiments are not uniformly reliable. When different experiments are designed to measure the same unknown quantity, how can their results be consolidated in an unbiased and optimal way? Given data from experiments made at different times, in different locations, with different methodologies, and perhaps differing even in underlying theory, is there a straightforward, easily applied method for combining the results from all of the experiments into a single distribution? This paper describes a new mathematical method called conflation for consolidating data from independent experiments that measure the same physical quantity.
Categorical Data Analysis
Number of Pages
This article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. The article appeared in Chaos 21 (3) 2011 and may be found at http://dx.doi.org/10.1063/1.3593373