A histogram is a representation of the distribution of numerical data. It is an estimate of the probability distribution for an uncertain output/s. A histogram is well known and understood. However, our approach allows something we call partial histograms. These partial histograms represent a partial part of the final histogram and their sum creates the final histogram. This allows visualizing how each part of the variable influences the final distribution.Influencer compares the final distribution against a distribution with several increment functions neglected from the final model. This allows a comparative visualization of the influence of the selected increment functions and focuses on the right aspects of the problem. For a better perspective, the final Probability Distribution Function is visualized on the background.
Fig. 1: Influencer – increment
The table under sets -> options refers to the increment function required to create the histogram. All the increment functions are selected by default and the increment function/functions of interest can be de-selected to see the change in the final distribution.
The graph is fully adjustable with options, where:
Fig. 2: Influencer – options
To store selected results in File (Upper left corner) select save. It will allow you to browse in folders starting in the project folder. The code automatically selects the format to store visualized results.
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