Statistical Decisions Utilizing Neural Nets
Memorandum rept. Jan-Mar 1990
ARMY ARMAMENT MUNITIONS AND CHEMICAL COMMAND ROCK ISLAND IL SYSTEMS ANALYSIS OFFICE
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Neural networks were developed that accurately determine the statistical characteristics modality and number fo stochastic components of underlying probability distributions for sample data. Sample data examples, used to teach the neural nets were generated utilizing either a single beta distribution or a mixture of beta distributions. Once the neural net learned to distinguish between unimodal and multimodal examples and also between unimodal and mixture densities, they were challenged with unknown test cases. The test cases were also generated from either a single beta distribution or a mixture of beta distributions. Therefore the initial test results apply to a restricted class of distributions having bounded domains. The initial testing of the neural networks consisted of 40 unknown sample data examples generated utilizing beta distributions. The results of these tests are 1 correctly identified 39 out of the 40 as being either unimodal or multimodal, an accuracy of 97.5 percent. This exceeds the accuracy of currently available statistical methods 2 correctly identified 36 out of 40 as having either one component or more than one component, an accuracy of 90 percent.
- Statistics and Probability