The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
The variational approximation for Bayesian inference
Abstract
The influence of this Thomas Bayes' work was immense. It was from here that "Bayesian" ideas first spread through the mathematical world, as Bayes's own article was ignored until 1780 and played no important role in scientific debate until the 20th century. It was also this article of Laplace's that introduced the mathematical techniques for the asymptotic analysis of posterior distributions that are still employed today. And it was here that the earliest example of optimum estimation can be found, the derivation and characterization of an estimator that minimized a particular measure of posterior expected loss. After more than two centuries, we mathematicians, statisticians cannot only recognize our roots in this masterpiece of our science, we can still learn from it.
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Citations
Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
An Introduction to Variational Methods for Graphical Models
Jordan, M. I., Ghahramani, Z., Jaakkola, T. S., & Saul, L. K. (1999) · Machine Learning · doi:10.1023/a:1007665907178 · Cited by 3,791
An introduction to MCMC for machine learning.
Andrieu, C., de Freitas, N., Doucet, A., & Jordan, M. I. (2003) · Machine Learning · doi:10.1023/a:1020281327116 · Cited by 2,451
A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants
Neal, R. M., & Hinton, G. E. (1998) · Learning in Graphical Models · doi:10.1007/978-94-011-5014-9_12 · Cited by 2,187
Itzykson, C., & Drouffe, J.-M. (1989) · doi:10.1017/cbo9780511622779 · Cited by 885
Variational Bayes for generalized autoregressive models
Roberts, S. J., & Penny, W. D. (2002) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2002.801921 · Cited by 157
Cited by
A Novel Adaptive Kalman Filter With Inaccurate Process and Measurement Noise Covariance Matrices
Huang, Y., Zhang, Y., Wu, Z., Li, N., & Chambers, J. (2018) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2017.2730480 · Cited by 731
Metadata
- Authors
- Tzikas, D. G., Likas, A. C., & Galatsanos, N. P.
- Venue
- IEEE Signal Processing Magazine
- Published
- 2008-11
- Volume
- 25
- Issue
- 6
- Pages
- 131-146
- ISSN
- 1053-5888 (ISSN-L)
- 1558-0792
- OpenAlex
- W2000721204
- Cited by
- 1,039
- References
- 6
- FWCI
- 11.49
- Type
- journal article
- Language
- en
- Open access
- closed
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- Tzikas, D. G., Likas, A. C., & Galatsanos, N. P. (2008). The variational approximation for Bayesian inference. IEEE Signal Processing Magazine. https://doi.org/10.1109/msp.2008.929620
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
