VIBES is a software package which allows variational inference to be performed automatically on a Bayesian network (if the terms in italics don’t mean anything to you, read this tutorial before continuing). I created VIBES during my Ph.D. as an implementation of my Variational Message Passing algorithm.
Keywords for this software
References in zbMATH (referenced in 5 articles )
Showing results 1 to 5 of 5.
- Liu, Han; Wang, Lie: TIGER: A tuning-insensitive approach for optimally estimating Gaussian graphical models (2017)
- Zhang, Haixiang; Zheng, Yinan; Yoon, Grace; Zhang, Zhou; Gao, Tao; Joyce, Brian; Zhang, Wei; Schwartz, Joel; Vokonas, Pantel; Colicino, Elena; Baccarelli, Andrea; Hou, Lifang; Liu, Lei: Regularized estimation in sparse high-dimensional multivariate regression, with application to a DNA methylation study (2017)
- Luttinen, Jaakko: BayesPy: variational Bayesian inference in Python (2016)
- Sizov, Sergej; Ens, Andreas: Eventfolk - automatische erkennung von ereignissen in sozialen medien (2010) ioport
- Titterington, D.M.: Bayesian methods for neural networks and related models (2004)