Variational inference where the model factorizes over some graphical independence structure. TBD.
Frey, B. J., and Nebojsa Jojic. 2005. “A Comparison of Algorithms for Inference and Learning in Probabilistic Graphical Models.” IEEE Transactions on Pattern Analysis and Machine Intelligence 27 (9): 1392–1416. https://doi.org/10.1109/TPAMI.2005.169.
Jordan, Michael I., Zoubin Ghahramani, Tommi S. Jaakkola, and Lawrence K. Saul. 1998. “An Introduction to Variational Methods for Graphical Models.” In Learning in Graphical Models, edited by Michael I. Jordan, 105–61. Dordrecht: Springer Netherlands. https://doi.org/10.1007/978-94-011-5014-9_5.
———. 1999. “An Introduction to Variational Methods for Graphical Models.” Machine Learning 37 (2): 183–233. https://doi.org/10.1023/A:1007665907178.
Murphy, Kevin P. 2012. Machine Learning: A Probabilistic Perspective. 1 edition. Adaptive Computation and Machine Learning Series. Cambridge, MA: MIT Press.
Roychowdhury, Anirban, and Brian Kulis. 2015. “Gamma Processes, Stick-Breaking, and Variational Inference.” In Artificial Intelligence and Statistics, 800–808. PMLR. http://proceedings.mlr.press/v38/roychowdhury15.html.
Wainwright, Martin J., and Michael I. Jordan. 2008. Graphical Models, Exponential Families, and Variational Inference. Vol. 1. Foundations and Trends® in Machine Learning. Now Publishers. https://doi.org/10.1561/2200000001.
Wainwright, Martin, and Michael I Jordan. 2005. “A Variational Principle for Graphical Models.” In New Directions in Statistical Signal Processing. Vol. 155. MIT Press.
Winn, John M., and Christopher M. Bishop. 2005. “Variational Message Passing.” In Journal of Machine Learning Research, 661–94. http://johnwinn.org/Publications/papers/VMP2005.pdf.
Yoshida, Ryo, and Mike West. 2010. “Bayesian Learning in Sparse Graphical Factor Models via Variational Mean-Field Annealing.” Journal of Machine Learning Research 11 (May): 1771–98. http://www.jmlr.org/papers/v11/yoshida10a.html.