[R-sig-Geo] Bayesian Maximum Entropy insights & tips?

[Tim Peterson]

Dear listers,

I've recently become aware of Bayesian Maximum Entropy (BME) 
geostatistical mapping and I am especially interested in its ability to 
incorporate soft-data intervals into the mapping. The inclusion of such 
constraints on the estimates appears to be much easier, and less 
computationally demanding, that using indicator kriging/simulation.

I have read many of the BME papers by George Christakos and co and the 
few application papers by others. However, despite being conformable 
with kriging equations etc, to me the BME methods are opaque and I am 
yet to find a paper clearly presenting an application. Furthermore, 
there is no R BME package (only software appears to be compiled matlab 
MEX library 'BMElib') and almost no discussion of BME on this list.

So anyway, I'd be really grateful to get any sights or experience with 
BME, application within R and references to detailed application papers.

Thanks in advance,

Tim

--------------------
Dr. Tim Peterson

The Department of Infrastructure Engineering
The University of Melbourne, 3010 Australia
T: +61 3 8344 9950 <tel:%2B61%203%208344%209950>, M: +61 0438 385 937 
<tel:%2B61%200438%20385%20937>

Find out more: Dept. profile 
<http://www.ie.unimelb.edu.au/people/staff.php?person_ID=141135> | 
Research Gate <https://www.researchgate.net/profile/Tim_Peterson7> | 
Google Scholar 
<http://scholar.google.com.au/citations?user=kkYJLF4AAAAJ&hl=en&oi=ao> | 
Groundwater Statistics Toolbox 
<http://peterson-tim-j.github.io/Groundwater-Statistics-Toolbox/> | 
Catchment Resilience Model 
<https://github.com/peterson-tim-j/Catchment_Resilience_Model>

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