A program for the Bayesian neural network in the ROOT framework We present a Bayesian Neural Network algorithm implemented in the TMVA package (Hoecker et al., 2007 [1]), within the ROOT framework (Brun and Rademakers, 1997 [2]). Comparing to the conventional utilization of Neural Network as discriminator, this new implementation has more advantages as a non-parametric regression tool, particularly for fitting probabilities. It provides functionalities including cost function selection, complexity control and uncertainty estimation. An example of such application in High Energy Physics is shown. The algorithm is available with ROOT release later than 5.29.

References in zbMATH (referenced in 30 articles , 1 standard article )

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  1. Borzou, Ahmad: A macroscopically effective Lorentz gauge theory of gravity (2016)
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  14. Zhong, Jiahang; Huang, Run-Sheng; Lee, Shih-Chang: A program for the Bayesian neural network in the ROOT framework (2011)
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