Macromolecular diffraction data: maximum entropy Patterson calculation used to estimate unobserved data

Integration of macromolecular diffraction data.

Diffraction intensities can be evaluated by two distinct procedures: summation integration and profile fitting. Equations are derived for evaluating the intensities and their standard errors for both cases, based on Poisson statistics. These equations highlight the importance of the contribution of the X-ray background to the standard error and give an estimate of the improvement which can be a...

Maximum Entropy Data Analysis

The integration of macromolecular diffraction data.

The objective of any modern data-processing program is to produce from a set of diffraction images a set of indices (hkls) with their associated intensities (and estimates of their uncertainties), together with an accurate estimate of the crystal unit-cell parameters. This procedure should not only be reliable, but should involve an absolute minimum of user intervention. The process can be conv...

Imagi,ng of Diffraction Data by the Maximum Entropy Method -anew Approach to Crystallography-

The recent works of the Maximum Entropy Method (MEM) in the field of accurate crystal structure analysis were reviewed, The role of MEM analysis in these works are described by the concept of Imaging of Diffraction Data (IDD). Assuming that the phase problem is solved by direct method or some other way, it is possible to construct the real space image, such as the electron density distribution ...

The Effect of Observation Data Sampling Methods on Infiltration Areas by Maximum Entropy Model

Statistical modeling methods are based on multivariate regression methods and require the presence and absence location of data for the construction of the model. In most cases, there is no trustworthy absence data. Therefore, other methods that are based only on the presence of the phenomenon are used. Considering the importance of modeling - saving time and cost and the probable prediction of...