Journal ArticleDOI
Novel approach to nonlinear/non-Gaussian Bayesian state estimation
Neil Gordon,David Salmond,Adrian F. M. Smith +2 more
- Vol. 140, Iss: 2, pp 107-113
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TLDR
An algorithm, the bootstrap filter, is proposed for implementing recursive Bayesian filters, represented as a set of random samples, which are updated and propagated by the algorithm.Abstract:
An algorithm, the bootstrap filter, is proposed for implementing recursive Bayesian filters. The required density of the state vector is represented as a set of random samples, which are updated and propagated by the algorithm. The method is not restricted by assumptions of linear- ity or Gaussian noise: it may be applied to any state transition or measurement model. A simula- tion example of the bearings only tracking problem is presented. This simulation includes schemes for improving the efficiency of the basic algorithm. For this example, the performance of the bootstrap filter is greatly superior to the standard extended Kalman filter.read more
Citations
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Journal ArticleDOI
Lookahead Strategies for Sequential Monte Carlo
Ming Lin,Rong Chen,Jun Liu +2 more
TL;DR: The main idea is to allow for lookahead in the Monte Carlo process so that future information can be utilized in weighting and generating Monte Carlo samples, or resampling from samples of the current state.
A Survey of Visual Tracking
Hou Zhi-qiang,Han Chongzhao +1 more
TL;DR: The applications of visual tracking in three areas including visual surveillance, image compression, and 3-D reconstruction are discussed and the state of the art about visual tracking is introduced, especially the main approaches ofVisual tracking are shown.
Journal ArticleDOI
Key epidemiological drivers and impact of interventions in the 2020 SARS-CoV-2 epidemic in England.
Edward Knock,Edward Knock,Lilith K Whittles,Lilith K Whittles,Lilith K Whittles,John A. Lees,Pablo N Perez-Guzman,Robert Verity,Richard G. FitzJohn,Katy A. M. Gaythorpe,Natsuko Imai,Wes Hinsley,Lucy C Okell,Alicia Rosello,Nikolas Kantas,Caroline E. Walters,Sangeeta N. Bhatia,Oliver J Watson,Charles Whittaker,Lorenzo Cattarino,A Boonyasiri,Bimandra A Djaafara,Keith J. Fraser,Han Fu,Haowei Wang,Xiaoyue Xi,Christl A. Donnelly,Christl A. Donnelly,Elita Jauneikaite,Daniel J Laydon,Peter J White,Peter J White,Peter J White,Azra C. Ghani,Neil M. Ferguson,Neil M. Ferguson,Anne Cori,Anne Cori,Marc Baguelin,Marc Baguelin,Marc Baguelin +40 more
TL;DR: In this article, the authors fitted a model of SARS-CoV-2 transmission in care homes and the community to regional surveillance data for England, and provided a synthesis of multiple surveillance data streams into a single coherent modeling framework, allowing transmission and severity to be disentangled from features of the system.
Journal ArticleDOI
A global sensitivity analysis and Bayesian inference framework for improving the parameter estimation and prediction of a process-based Terrestrial Ecosystem Model
Jinyun Tang,Qianlai Zhuang +1 more
TL;DR: In this paper, a global sensitivity analysis and Bayesian inference framework was developed for improving the parameterization and predictability of a monthly time step process-based biogeochemistry model.
Journal ArticleDOI
A Partially Observable Markov Decision Process Approach to Residential Home Energy Management
Timothy M. Hansen,Edwin K. P. Chong,Siddharth Suryanarayanan,Anthony A. Maciejewski,Howard Jay Siegel +4 more
TL;DR: This work presents three new HEMS techniques—one myopic approach and two non-myopic partially observable Markov decision process (POMDP) approaches—for minimizing the household electricity bill in such a RTP market and shows that the non- myopic POMDP approach can provide a 10%–30% saving over the status quo.
References
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BookDOI
Density estimation for statistics and data analysis
TL;DR: The Kernel Method for Multivariate Data: Three Important Methods and Density Estimation in Action.
Book
Stochastic Processes and Filtering Theory
TL;DR: In this paper, a unified treatment of linear and nonlinear filtering theory for engineers is presented, with sufficient emphasis on applications to enable the reader to use the theory for engineering problems.
Journal ArticleDOI
Nonlinear Bayesian estimation using Gaussian sum approximations
D. Alspach,H. Sorenson +1 more
TL;DR: In this paper an approximation that permits the explicit calculation of the a posteriori density from the Bayesian recursion relations is discussed and applied to the solution of the nonlinear filtering problem.
Journal Article
Bayesian statistics without tears: A sampling-resampling perspective
TL;DR: In this article, a sampling-resampling perspective on Bayesian inference is presented, which has both pedagogic appeal and suggests easily implemented calculation strategies, such as sampling-based methods.