Bayesian inference is one of two dominant approaches to statistical inference. The word Bayesian refers to the influence of Reverend Thomas Bayes. Bayesian inference is a modern revival of the classical definition of probability.
P. Chapman, G. Stapleton, J. Howse, and I. Oliver. 2011 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)
, page 87-94. (September 2011)
A. Morabia. American journal of epidemiology178
(10):
1526-32 (November 2013)JID: 7910653; OTO: NOTNLM; aheadofprint;<m:linebreak></m:linebreak>Causalitat.
V. Alexiev. Workshop on Semantic Digital Archives (SDA 2012), part of International Conference on Theory and Practice of Digital Libraries (TPDL 2012)
, 912, Paphos, Cyprus, CEUR WS, (September 2012)
V. Alexiev, D. Manov, J. Parvanova, and S. Petrov. Workshop Practical Experiences with CIDOC CRM and its Extensions (CRMEX 2013) at TPDL 2013
, 1117, Valetta, Malta, CEUR WS, (September 2013)
G. Marinov, V. Alexiev, and Y. Djonev. Artifical Intelligence: Methodology, Systems, and Applications (AIMSA'94)
, page 109-118. Sofia, Bulgaria, World Scientific Publishing, (September 1994)
X. Zheng, C. Dan, B. Aragam, P. Ravikumar, and E. Xing. Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics
, volume 108 of Proceedings of Machine Learning Research, page 3414--3425. PMLR, (26--28 Aug 2020)
J. Huggins, M. Kasprzak, T. Campbell, and T. Broderick. (2019)cite arxiv:1910.04102Comment: A python package for carrying out our validated variational inference workflow -- including doing black-box variational inference and computing the bounds we develop in this paper -- is available at https://github.com/jhuggins/viabel. The same repository also contains code for reproducing all of our experiments.