The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Particle Learning and Smoothing
Open accessAbstract
Particle learning (PL) provides state filtering, sequential parameter learning and smoothing in a general class of state space models. Our approach extends existing particle methods by incorporating the estimation of static parameters via a fully-adapted filter that utilizes conditional sufficient statistics for parameters and/or states as particles. State smoothing in the presence of parameter uncertainty is also solved as a by-product of PL. In a number of examples, we show that PL outperforms existing particle filtering alternatives and proves to be a competitor to MCMC.
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Cites
A New Approach to Linear Filtering and Prediction Problems
Kalman, R. E. (1960) · Journal of Basic Engineering · doi:10.1115/1.3662552 · Cited by 31,751
Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference, Second Edition
Gamerman, D., & Lopes, H. F. (2006) · doi:10.1201/9781482296426 · Cited by 1,097
Self-citation
Cited by
A Survey of Sequential Monte Carlo Methods for Economics and Finance
Creal, D. (2012) · Econometric Reviews · doi:10.1080/07474938.2011.607333 · Cited by 301
Dynamic Trees for Learning and Design
Taddy, M. A., Gramacy, R. B., & Polson, N. G. (2011) · Journal of the American Statistical Association · doi:10.1198/jasa.2011.ap09769 · Cited by 113
Self-citationSun, J., Zuo, H., & Pecht, M. G. (2011) · 2011 Prognostics and System Health Managment Confernece · doi:10.1109/phm.2011.5939505 · Cited by 7
Metadata
- Authors
- Carvalho, C. M., Johannes, M. S., Lopes, H. F., & Polson, N. G.
- Venue
- Statistical Science
- Publisher
- Institute of Mathematical Statistics
- Published
- 2010-02-01
- Volume
- 25
- Issue
- 1
- Pages
- 88-106
- ISSN
- 0883-4237 (ISSN-L)
- OpenAlex
- W2033444885
- Cited by
- 230
- References
- 2
- FWCI
- 11.04
- Type
- journal article
- Language
- en
- Open access
- bronze
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
