The Bayesian maximum entropy method for lognormal variables |
| |
Authors: | T G Orton R M Lark |
| |
Institution: | (1) Rothamsted Research, Harpenden, Hertfordshire, AL5 2JQ, UK |
| |
Abstract: | The Bayesian maximum entropy (BME) method can be used to predict the value of a spatial random field at an unsampled location
given precise (hard) and imprecise (soft) data. It has mainly been used when the data are non-skewed. When the data are skewed,
the method has been used by transforming the data (usually through the logarithmic transform) in order to remove the skew.
The BME method is applied for the transformed variable, and the resulting posterior distribution transformed back to give
a prediction of the primary variable. In this paper, we show how the implementation of the BME method that avoids the use
of a transform, by including the logarithmic statistical moments in the general knowledge base, gives more appropriate results,
as expected from the maximum entropy principle. We use a simple illustration to show this approach giving more intuitive results,
and use simulations to compare the approaches in terms of the prediction errors. The simulations show that the BME method
with the logarithmic moments in the general knowledge base reduces the errors, and we conclude that this approach is more
suitable to incorporate soft data in a spatial analysis for lognormal data. |
| |
Keywords: | BME Skewed data Lognormal variables |
本文献已被 SpringerLink 等数据库收录! |
|