A program for the Bayesian Neural Network in the ROOT framework

Published: 1 December 2011| Version 1 | DOI: 10.17632/6kb9w7yd8b.1
Jiahang Zhong, Run-Sheng Huang, Shih-Chang Lee


Abstract 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 app... Title of program: TMVA-BNN Catalogue Id: AEJX_v1_0 Nature of problem Non-parametric fitting of multivariate distributions. Versions of this program held in the CPC repository in Mendeley Data AEJX_v1_0; TMVA-BNN; 10.1016/j.cpc.2011.07.019 This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2018)



Computational Physics, Elementary Particles