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We apply Bayesian Additive Regression Tree (BART) principles to training an ensemble of small neural networks for regression tasks. Using Markov Chain Monte Carlo, Bayesian Additive Regression Net- works (BARN) samples from the space of single hidden layer neural networks that are conditioned on their fit to data. You can find more information in [barnpy](https://github.com/dvbuntu/barmpy) and its associated [documentation](https://dvbuntu.github.io/barmpy/)" +Description: Bayesian Additive Regression Networks. We apply Bayesian Additive + Regression Tree (BART) principles to training an ensemble of small neural + networks for regression tasks. Using Markov Chain Monte Carlo, Bayesian + Additive Regression Net- works (BARN) samples from the space of single hidden + layer neural networks that are conditioned on their fit to data. You can find + more information in [barnpy](https://github.com/dvbuntu/barmpy) and its + associated [documentation](https://dvbuntu.github.io/barmpy/) tags: - Python libraries github: https://github.com/dvbuntu/barn -link: -image: /assets/images/softwares/barn.png +link: null +image: /assets/images/softwares/barmpy_logo.png imageAlt: BARN image ---- \ No newline at end of file +order: 60 +---