The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
On-Line Expectation–Maximization Algorithm for latent Data Models
Open accessAbstract
Summary: We propose a generic on-line (also sometimes called adaptive or recursive) version of the expectation–maximization (EM) algorithm applicable to latent variable models of independent observations. Compared with the algorithm of Titterington, this approach is more directly connected to the usual EM algorithm and does not rely on integration with respect to the complete-data distribution. The resulting algorithm is usually simpler and is shown to achieve convergence to the stationary points of the Kullback–Leibler divergence between the marginal distribution of the observation and the model distribution at the optimal rate, i.e. that of the maximum likelihood estimator. In addition, the approach proposed is also suitable for conditional (or regression) models, as illustrated in the case of the mixture of linear regressions model.
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Cites
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On the Convergence Properties of the EM Algorithm
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Statistical Analysis of Finite Mixture Distributions.
Lindsay, B. G., Titterington, D. M., Smith, A. F. M., & Makov, U. E. (1987) · Journal of the American Statistical Association · doi:10.2307/2289504 · Cited by 2,905
Hierarchical Mixtures of Experts and the EM Algorithm
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A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants
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Stochastic Approximation and Recursive Algorithms and Applications
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Bianco, M. J., Gerstoft, P., Traer, J., Ozanich, E., Roch, M. A., Gannot, S., & Deledalle, C.-A. (2019) · The Journal of the Acoustical Society of America · doi:10.1121/1.5133944 · Cited by 609
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Metadata
- Authors
- Cappé, O., & Moulines, E.
- Venue
- Journal of the Royal Statistical Society Series B: Statistical Methodology
- Publisher
- Oxford University Press (OUP)
- Published
- 2009-02-20
- Volume
- 71
- Issue
- 3
- Pages
- 593-613
- ISSN
- 1369-7412 (ISSN-L)
- 1467-9868
- OpenAlex
- W1686266550
- Cited by
- 477
- References
- 27
- FWCI
- 22.77
- Type
- journal article
- Language
- en
- Open access
- bronze
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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.
