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 31 articles , 1 standard article )

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  1. Ko, P.; Natale, Alexander; Park, Myeonghun; Yokoya, Hiroshi: Simplified DM models with the full SM gauge symmetry: the case of $t$-channel colored scalar mediators (2017)
  2. Borzou, Ahmad: A macroscopically effective Lorentz gauge theory of gravity (2016)
  3. Thomas Keck: FastBDT: A speed-optimized and cache-friendly implementation of stochastic gradient-boosted decision trees for multivariate classification (2016) arXiv
  4. August, Daniel; Maas, Axel: On the Landau-gauge adjoint quark propagator (2013)
  5. Hashi, Manami; Kitazawa, Noriaki: Signatures of low-scale string models at the LHC (2012)
  6. R. Aloisio, D. Boncioli, A. F. Grillo, S. Petrera, F. Salamida: SimProp: a Simulation Code for Ultra High Energy Cosmic Ray Propagation (2012) arXiv
  7. Širca, Simon; Horvat, Martin: Computational methods for physicists. Compendium for students. Translated from the Slovenian. (2012)
  8. Allanach, Benjamin C.; Grab, Sebastian; Haber, Howard E.: Supersymmetric monojets at the Large Hadron Collider (2011)
  9. Çobanoǧlu, Özgür; Özcan, Erkcan; Sultansoy, Saleh; Ünel, Gökhan: OPUCEM: a library with error checking mechanism for computing oblique parameters (2011)
  10. Maas, Axel: On the gauge boson’s properties in a candidate technicolor theory (2011)
  11. Maas, Axel: On the gauge-algebra dependence of Landau-gauge Yang-Mills propagators (2011)
  12. Mandal, Sourav K.; Nojiri, Mihoko; Sudano, Matthew; Yanagida, Tsutomu T.: Testing the Nambu-Goldstone hypothesis for quarks and leptons at the LHC (2011)
  13. Morháč, Miroslav; Matoušek, Vladislav: High-resolution boosted deconvolution of spectroscopic data (2011)
  14. 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) ioport
  15. Zhong, Jiahang; Huang, Run-Sheng; Lee, Shih-Chang: A program for the Bayesian neural network in the ROOT framework (2011)
  16. Carasco, C.: MCNP output data analysis with ROOT (MODAR) (2010)
  17. Godbole, Rohini M.; Vempati, Sudhir K.; Wingerter, Akın: Four generations: SUSY and SUSY breaking (2010)
  18. 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)
  19. 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) ioport
  20. Slawinska, M.; Jadach, S.: MCdevelop - the universal framework for Stochastic Simulations (2010) arXiv

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