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 26 articles , 1 standard article )

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  1. Borzou, Ahmad: A macroscopically effective Lorentz gauge theory of gravity (2016)
  2. August, Daniel; Maas, Axel: On the Landau-gauge adjoint quark propagator (2013)
  3. Hashi, Manami; Kitazawa, Noriaki: Signatures of low-scale string models at the LHC (2012)
  4. Širca, Simon; Horvat, Martin: Computational methods for physicists. Compendium for students. Translated from the Slovenian. (2012)
  5. Allanach, Benjamin C.; Grab, Sebastian; Haber, Howard E.: Supersymmetric monojets at the Large Hadron Collider (2011)
  6. Çobanoǧlu, Özgür; Özcan, Erkcan; Sultansoy, Saleh; Ünel, Gökhan: OPUCEM: a library with error checking mechanism for computing oblique parameters (2011)
  7. Maas, Axel: On the gauge-algebra dependence of Landau-gauge Yang-Mills propagators (2011)
  8. Maas, Axel: On the gauge boson’s properties in a candidate technicolor theory (2011)
  9. Mandal, Sourav K.; Nojiri, Mihoko; Sudano, Matthew; Yanagida, Tsutomu T.: Testing the Nambu-Goldstone hypothesis for quarks and leptons at the LHC (2011)
  10. Morháč, Miroslav; Matoušek, Vladislav: High-resolution boosted deconvolution of spectroscopic data (2011)
  11. Torres, Rodrigo Coura; dos Anjos, Andre Rabello; de Seixas, José Manoel; Solovier, Igor: Automatizing the online filter test management for a general-purpose particle detector (2011)
  12. Zhong, Jiahang; Huang, Run-Sheng; Lee, Shih-Chang: A program for the Bayesian neural network in the ROOT framework (2011)
  13. Carasco, C.: MCNP output data analysis with ROOT (MODAR) (2010)
  14. Godbole, Rohini M.; Vempati, Sudhir K.; Wingerter, Akın: Four generations: SUSY and SUSY breaking (2010)
  15. Lundberg, J.; Conrad, J.; Rolke, W.; Lopez, A.: Limits, discovery and cut optimization for a Poisson process with uncertainty in background and signal efficiency: TRolke 2.0 (2010)
  16. Riede, Moritz; Schueppel, Rico; Sylvester-Hvid, Kristian O.; Kühne, Martin; Röttger, Michael C.; Zimmermann, Klaus; Liehr, Andreas W.: On the communication of scientific data: The full-metadata format (2010)
  17. Antcheva, I.; Ballintijn, M.; Bellenot, B.; Biskup, M.; Brun, R.; Buncic, N.; Canal, Ph.; Casadei, D.; Couet, O.; Fine, V.; Franco, L.; Ganis, G.; Gheata, A.; Maline, D.Gonzalez; Goto, M.; Iwaszkiewicz, J.; Kreshuk, A.; Segura, D.Marcos; Maunder, R.; Moneta, L.; Al.: ROOT - a C++ framework for petabyte data storage, statistical analysis and visualization (2009)
  18. Lang, Duncan Temple: A modest proposal: an approach to making the internal R system extensible (2009)
  19. Mościcki, J.T.; Brochu, F.; Ebke, J.; Egede, U.; Elmsheuser, J.; Harrison, K.; Jones, R.W.L.; Lee, H.C.; Liko, D.; Maier, A.; Muraru, A.; Patrick, G.N.; Pajchel, K.; Reece, W.; Samset, B.H.; Slater, M.W.; Soroko, A.; Tan, C.L.; Van Der Ster, D.C.; Williams, M.: Ganga: A tool for computational-task management and easy access to grid resources (2009)
  20. Fletcher, Martyn; Liang, Bojian; Smith, Leslie; Knowles, Alastair; Jackson, Tom; Jessop, Mark; Austin, Jim: Neural network based pattern matching and spike detection tools and services -- in the CARMEN neuroinformatics project (2008)

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