Principled Poisson Image "Deconvolution"/"Reconstruction" with Error Bars + Significance Tests

NEW!! E-EMC2 Dec 2008 Simpler test runs, associated small python utilities. A. Connors

These are test files for the principled image deconvolution, fitting, and feature-detection/extraction method based on Esch et. al. The code can be obtained from David van Dyk at University of California, Irvine; or Nathan Stein at the Harvard Statistics Department.

Thanks to Nathan Stein for sugesting this simpler test format. The test image is 8x8, with a 5x3 "E" within it, on a constant background. If your set-up is working, you should see the "E" clearly in the mean and less clearly in the individual MCMC samples , when using a flat or no background. Nothing significant should be visible when using the 'true' null model as background.

The gzipped tar file is HERE . "gunzip" it, extract the files, and read the AAA_EEMC2_READ.ME file. Please let us know (aconnors AT eurekabayes.com) if you have questions or problems.

EMC2 Dec 2006 Version test runs, associated small python utilities. A. Connors