Online Program

WITHDRAWN: BLNN: An R Package for Training Neural Networks (308325)

Keshav Pokhrel, University of Michigan Dearborn 
Taysseer Sharaf, University of Michigan Dearborn 
Theren Williams, University of Illinois Urbana-Champaign 

Keywords: R Bayesian Neural Networks, HMC sampling, No-U-Turn,Evidence procedure, Bayesian Learning, Statistical Learning

The Bayesian Learning for Neural Networks (BLNN) package coalesces the predictive power of neural networks with a breadth of Bayesian sampling techniques for the first time in R. BLNN offers users Hamiltonian Monte Carlo (HMC) and No-U-Turn (NUTS) sampling algorithms with dual averaging for posterior weight generation. A robust implementation of hyper-parameters and optional re-estimation through the evidence procedure gives BLNN high predictive precision. BLNN is compatible with RStan diagnostic tool ShinyStan. BLNN can be used in a wide range of applications which are based on developing statistical models such as multiple linear and logistic regression, classification, and survival analysis.