The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Variational inference for Dirichlet process mixtures
Open accessAbstract
Dirichlet process (DP) mixture models are the cornerstone of nonparametric Bayesian statistics, and the development of Monte-Carlo Markov chain (MCMC) sampling methods for DP mixtures has enabled the application of nonparametric Bayesian methods to a variety of practical data analysis problems. However, MCMC sampling can be prohibitively slow, and it is important to explore alternatives. One class of alternatives is provided by variational methods, a class of deterministic algorithms that convert inference problems into optimization problems (Opper and Saad 2001; Wainwright and Jordan 2003). Thus far, variational methods have mainly been explored in the parametric setting, in particular within the formalism of the exponential family (Attias 2000; Ghahramani and Beal 2001; Blei et al. 2003). In this paper, we present a variational inference algorithm for DP mixtures. We present experiments that compare the algorithm to Gibbs sampling algorithms for DP mixtures of Gaussians and present an application to a large-scale image analysis problem.
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Cites
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Self-citationGraphical Models, Exponential Families, and Variational Inference
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Research works printed but not linked: 3
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- Xing, E., M. Jordan, and S. Russell. 2003. A generalized mean field algorithm for variational inference in exponential families. In Proceedings of the 19th Annual Conference on Uncertainty in Artificial Intelligence (UAI-03), eds. C. Meek and U. Kjærulff, 583–591. San Francisco, CA: Morgan Kaufmann Publishers.
Cited by
Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts
Grimmer, J., & Stewart, B. M. (2013) · Political Analysis · doi:10.1093/pan/mps028 · Cited by 3,297
Modelling uncertainty in deep learning for camera relocalization
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“However we found that all of these methods produced poorer localization uncertainty than fitting a single unimodal Gaussian to the data.”
MethodAutomatic differentiation variational inference
Kucukelbir, A., Tran, D., Ranganath, R., Gelman, A., & Blei, D. M. (2016) · arXiv · doi:10.48550/arxiv.1603.00788 · Cited by 515
A comparative evaluation of outlier detection algorithms: Experiments and analyses
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Fast hierarchical Bayesian analysis of population structure
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Metadata
- Authors
- Blei, D. M., & Jordan, M. I.
- Venue
- Bayesian Analysis
- Publisher
- Institute of Mathematical Statistics
- Published
- 2006-03-01
- Volume
- 1
- Issue
- 1
- Pages
- 121-143
- ISSN
- 1931-6690 (ISSN-L)
- 1936-0975
- OpenAlex
- W2127498532
- Cited by
- 1,438
- References
- 30
- FWCI
- 27.94
- Type
- journal article
- Language
- en
- Open access
- diamond
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