The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Sequential Monte Carlo Methods for Dynamic Systems
Abstract
We provide a general framework for using Monte Carlo methods in dynamic systems and discuss its wide applications. Under this framework, several currently available techniques are studied and generalized to accommodate more complex features. All of these methods are partial combinations of three ingredients: importance sampling and resampling, rejection sampling, and Markov chain iterations. We provide guidelines on how they should be used and under what circumstance each method is most suitable. Through the analysis of differences and connections, we consolidate these methods into a generic algorithm by combining desirable features. In addition, we propose a general use of Rao-Blackwellization to improve performance. Examples from econometrics and engineering are presented to demonstrate the importance of Rao–Blackwellization and to compare different Monte Carlo procedures.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A tutorial on hidden Markov models and selected applications in speech recognition
Rabiner, L. R. (1989) · Proceedings of the IEEE · doi:10.1109/5.18626 · Cited by 22,743
Multiple Imputation for Nonresponse in Surveys
Rubin, D. B. (1987) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316696 · Cited by 20,701
Monte Carlo sampling methods using Markov chains and their applications.
Hastings, W. K. (1970) · Biometrika · doi:10.1093/biomet/57.1.97 · Cited by 15,195
Sampling-based approaches to calculating marginal densities
Gelfand, A. E., & Smith, A. F. M. (1990) · Journal of the American Statistical Association · doi:10.1080/01621459.1990.10476213 · Cited by 6,606
Cited by
Kalman Filtering and Neural Networks
(2001) · Wiley eBooks · doi:10.1002/0471221546 · Cited by 2,386
Robust Monte Carlo localization for mobile robots
Thrun, S., Fox, D., Burgard, W., & Dellaert, F. (2001) · Artificial Intelligence · doi:10.1016/s0004-3702(01)00069-8 · Cited by 1,809
Toni, T., Welch, D., Strelkowa, N., Ipsen, A., & Stumpf, M. P. H. (2009) · Journal of The Royal Society Interface · doi:10.1098/rsif.2008.0172 · Cited by 1,498
Sequential Monte Carlo Samplers
Del Moral, P., Doucet, A., & Jasra, A. (2006) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2006.00553.x · Cited by 1,239
Gaussian Process Dynamical Models for Human Motion
Wang, J. M., Fleet, D. J., & Hertzmann, A. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1167 · Cited by 1,020
Metadata
- Authors
- Liu, J. S., & Chen, R.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 1998-09
- Volume
- 93
- Issue
- 443
- Pages
- 1032-1044
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W2064480843
- Cited by
- 2,191
- References
- 4
- FWCI
- 30.23
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Sequential Monte Carlo Methods for Dynamic Systems. https://app.cochat.ai/papers/10.1080/01621459.1998.10473765
- The paper
- Liu, J. S., & Chen, R. (1998). Sequential Monte Carlo Methods for Dynamic Systems. Journal of the American Statistical Association. https://doi.org/10.1080/01621459.1998.10473765
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
