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Journal articles on the topic 'Sequential Monte Carlo methods'

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Published: 4 June 2021
Last updated: 9 June 2022

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1

Diaconis, Persi. "Sequential Monte Carlo Methods in Practice." Journal of the American Statistical Association 98, no. 462 (June 2003): 496–97. http://dx.doi.org/10.1198/jasa.2003.s282.

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2

Sarkar, Pradipta. "Sequential Monte Carlo Methods in Practice." Technometrics 45, no. 1 (February 2003): 106. http://dx.doi.org/10.1198/tech.2003.s23.

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3

Everitt, Richard G., Richard Culliford, Felipe Medina-Aguayo, and Daniel J. Wilson. "Sequential Monte Carlo with transformations." Statistics and Computing 30, no. 3 (November 17, 2019): 663–76. http://dx.doi.org/10.1007/s11222-019-09903-y.

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AbstractThis paper examines methodology for performing Bayesian inference sequentially on a sequence of posteriors on spaces of different dimensions. For this, we use sequential Monte Carlo samplers, introducing the innovation of using deterministic transformations to move particles effectively between target distributions with different dimensions. This approach, combined with adaptive methods, yields an extremely flexible and general algorithm for Bayesian model comparison that is suitable for use in applications where the acceptance rate in reversible jump Markov chain Monte Carlo is low. We use this approach on model comparison for mixture models, and for inferring coalescent trees sequentially, as data arrives.
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4

Akkam Veettil, Dilshad R., and Kit Clark. "Bayesian Geosteering Using Sequential Monte Carlo Methods." Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description 61, no. 1 (February 1, 2020): 99–111. http://dx.doi.org/10.30632/pjv61n1-2020a4.

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5

Jasra, Ajay, and Pierre Del Moral. "Sequential Monte Carlo Methods for Option Pricing." Stochastic Analysis and Applications 29, no. 2 (February 25, 2011): 292–316. http://dx.doi.org/10.1080/07362994.2011.548993.

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6

Jasra, Ajay, and Arnaud Doucet. "Sequential Monte Carlo methods for diffusion processes." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 465, no. 2112 (September 11, 2009): 3709–27. http://dx.doi.org/10.1098/rspa.2009.0206.

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In this paper, we show how to use sequential Monte Carlo methods to compute expectations of functionals of diffusions at a given time and the gradients of these quantities w.r.t. the initial condition of the process. In some cases, via the exact simulation of the diffusion, there is no time discretization error, otherwise the methods use Euler discretization. We illustrate our approach on both high- and low-dimensional problems from optimal control and establish that our approach substantially outperforms standard Monte Carlo methods typically adopted in the literature. The methods developed here are appropriate for solving a certain class of partial differential equations as well as for option pricing and hedging.
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7

Kemp, Freda. "An Introduction to Sequential Monte Carlo Methods." Journal of the Royal Statistical Society: Series D (The Statistician) 52, no. 4 (December 2003): 694–95. http://dx.doi.org/10.1046/j.1467-9884.2003.t01-6-00383_8.x.

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8

Liu, Jun S., and Rong Chen. "Sequential Monte Carlo Methods for Dynamic Systems." Journal of the American Statistical Association 93, no. 443 (September 1998): 1032–44. http://dx.doi.org/10.1080/01621459.1998.10473765.

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9

Finke, Axel, Arnaud Doucet, and Adam M. Johansen. "Limit theorems for sequential MCMC methods." Advances in Applied Probability 52, no. 2 (June 2020): 377–403. http://dx.doi.org/10.1017/apr.2020.9.

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AbstractBoth sequential Monte Carlo (SMC) methods (a.k.a. ‘particle filters’) and sequential Markov chain Monte Carlo (sequential MCMC) methods constitute classes of algorithms which can be used to approximate expectations with respect to (a sequence of) probability distributions and their normalising constants. While SMC methods sample particles conditionally independently at each time step, sequential MCMC methods sample particles according to a Markov chain Monte Carlo (MCMC) kernel. Introduced over twenty years ago in [6], sequential MCMC methods have attracted renewed interest recently as they empirically outperform SMC methods in some applications. We establish an $\mathbb{L}_r$ -inequality (which implies a strong law of large numbers) and a central limit theorem for sequential MCMC methods and provide conditions under which errors can be controlled uniformly in time. In the context of state-space models, we also provide conditions under which sequential MCMC methods can indeed outperform standard SMC methods in terms of asymptotic variance of the corresponding Monte Carlo estimators.
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10

Eberle, Andreas, and Carlo Marinelli. "Stability of sequential Markov Chain Monte Carlo methods." ESAIM: Proceedings 19 (2007): 22–31. http://dx.doi.org/10.1051/proc:071905.

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11

Chen, Yuguo, Junyi Xie, and Jun S. Liu. "Stopping-time resampling for sequential Monte Carlo methods." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 67, no. 2 (April 2005): 199–217. http://dx.doi.org/10.1111/j.1467-9868.2005.00497.x.

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12

Qian, Gang, and Rama Chellappa. "Structure from Motion Using Sequential Monte Carlo Methods." International Journal of Computer Vision 59, no. 1 (August 2004): 5–31. http://dx.doi.org/10.1023/b:visi.0000020669.68126.4b.

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13

Hanif, Ayub, and Robert Elliott Smith. "State Space Modeling & Bayesian Inference with Computational Intelligence." New Mathematics and Natural Computation 11, no. 01 (March 2015): 71–101. http://dx.doi.org/10.1142/s1793005715500040.

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Recursive Bayesian estimation using sequential Monte Carlos methods is a powerful numerical technique to understand latent dynamics of nonlinear non-Gaussian dynamical systems. It enables us to reason under uncertainty and addresses shortcomings underlying deterministic systems and control theories which do not provide sufficient means of performing analysis and design. In addition, parametric techniques such as the Kalman filter and its extensions, though they are computationally efficient, do not reliably compute states and cannot be used to learn stochastic problems. We review recursive Bayesian estimation using sequential Monte Carlo methods highlighting open problems. Primary of these is the weight degeneracy and sample impoverishment problem. We proceed to detail synergistic computational intelligence sequential Monte Carlo methods which address this. We find that imbuing sequential Monte Carlos with computational intelligence has many advantages when applied to many application and problem domains.
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14

Freitas, J. F. G. de, M. Niranjan, A. H. Gee, and A. Doucet. "Sequential Monte Carlo Methods to Train Neural Network Models." Neural Computation 12, no. 4 (April 1, 2000): 955–93. http://dx.doi.org/10.1162/089976600300015664.

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We discuss a novel strategy for training neural networks using sequential Monte Carlo algorithms and propose a new hybrid gradient descent/sampling importance resampling algorithm (HySIR). In terms of computational time and accuracy, the hybrid SIR is a clear improvement over conventional sequential Monte Carlo techniques. The new algorithm may be viewed as a global optimization strategy that allows us to learn the probability distributions of the network weights and outputs in a sequential framework. It is well suited to applications involving on-line, nonlinear, and nongaussian signal processing. We show how the new algorithm outperforms extended Kalman filter training on several problems. In particular, we address the problem of pricing option contracts, traded in financial markets. In this context, we are able to estimate the one-step-ahead probability density functions of the options prices.
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15

Del Moral, Pierre, Arnaud Doucet, and Ajay Jasra. "On adaptive resampling strategies for sequential Monte Carlo methods." Bernoulli 18, no. 1 (February 2012): 252–78. http://dx.doi.org/10.3150/10-bej335.

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16

Chen, Yuguo, Persi Diaconis, Susan P. Holmes, and Jun S. Liu. "Sequential Monte Carlo Methods for Statistical Analysis of Tables." Journal of the American Statistical Association 100, no. 469 (March 2005): 109–20. http://dx.doi.org/10.1198/016214504000001303.

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17

Doucet, Arnaud, Mark Briers, and Stéphane Sénécal. "Efficient Block Sampling Strategies for Sequential Monte Carlo Methods." Journal of Computational and Graphical Statistics 15, no. 3 (September 2006): 693–711. http://dx.doi.org/10.1198/106186006x142744.

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18

Beskos, Alexandros, Ajay Jasra, Nikolas Kantas, and Alexandre Thiery. "On the convergence of adaptive sequential Monte Carlo methods." Annals of Applied Probability 26, no. 2 (April 2016): 1111–46. http://dx.doi.org/10.1214/15-aap1113.

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19

Arnold, Andrea, Daniela Calvetti, and Erkki Somersalo. "Linear multistep methods, particle filtering and sequential Monte Carlo." Inverse Problems 29, no. 8 (July 15, 2013): 085007. http://dx.doi.org/10.1088/0266-5611/29/8/085007.

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20

Bruno, Marcelo G. S. "Sequential Monte Carlo Methods for Nonlinear Discrete-Time Filtering." Synthesis Lectures on Signal Processing 6, no. 1 (January 25, 2013): 1–99. http://dx.doi.org/10.2200/s00471ed1v01y201303spr011.

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21

Guo, Dong, Xiaodong Wang, and Rong Chen. "New sequential Monte Carlo methods for nonlinear dynamic systems." Statistics and Computing 15, no. 2 (April 2005): 135–47. http://dx.doi.org/10.1007/s11222-005-6846-5.

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22

Beskos, Alexandros, Ajay Jasra, Ege A. Muzaffer, and Andrew M. Stuart. "Sequential Monte Carlo methods for Bayesian elliptic inverse problems." Statistics and Computing 25, no. 4 (June 11, 2015): 727–37. http://dx.doi.org/10.1007/s11222-015-9556-7.

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23

Kim, Sunghan, Mateo Aboy, and James McNames. "Pulse pressure variation tracking using sequential Monte Carlo methods." Biomedical Signal Processing and Control 8, no. 4 (July 2013): 333–40. http://dx.doi.org/10.1016/j.bspc.2013.01.008.

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24

Blevins, Jason R. "Sequential Monte Carlo Methods for Estimating Dynamic Microeconomic Models." Journal of Applied Econometrics 31, no. 5 (June 16, 2015): 773–804. http://dx.doi.org/10.1002/jae.2470.

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25

Li, Qing, Bashar I. Ahmad, and Simon J. Godsill. "Sequential Dynamic Leadership Inference Using Bayesian Monte Carlo Methods." IEEE Transactions on Aerospace and Electronic Systems 57, no. 4 (August 2021): 2039–52. http://dx.doi.org/10.1109/taes.2021.3054693.

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26

Cemgil, A. T., and B. Kappen. "Monte Carlo Methods for Tempo Tracking and Rhythm Quantization." Journal of Artificial Intelligence Research 18 (January 1, 2003): 45–81. http://dx.doi.org/10.1613/jair.1121.

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We present a probabilistic generative model for timing deviations in expressive music performance. The structure of the proposed model is equivalent to a switching state space model. The switch variables correspond to discrete note locations as in a musical score. The continuous hidden variables denote the tempo. We formulate two well known music recognition problems, namely tempo tracking and automatic transcription (rhythm quantization) as filtering and maximum a posteriori (MAP) state estimation tasks. Exact computation of posterior features such as the MAP state is intractable in this model class, so we introduce Monte Carlo methods for integration and optimization. We compare Markov Chain Monte Carlo (MCMC) methods (such as Gibbs sampling, simulated annealing and iterative improvement) and sequential Monte Carlo methods (particle filters). Our simulation results suggest better results with sequential methods. The methods can be applied in both online and batch scenarios such as tempo tracking and transcription and are thus potentially useful in a number of music applications such as adaptive automatic accompaniment, score typesetting and music information retrieval.
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27

Lux, Thomas. "Estimation of agent-based models using sequential Monte Carlo methods." Journal of Economic Dynamics and Control 91 (June 2018): 391–408. http://dx.doi.org/10.1016/j.jedc.2018.01.021.

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28

Bishwal, Jaya P. N. "Sequential Monte Carlo methods for stochastic volatility models: a review." Journal of Interdisciplinary Mathematics 13, no. 6 (December 2010): 619–35. http://dx.doi.org/10.1080/09720502.2010.10700723.

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29

Jaward, M. H., D. Bull, and N. Canagarajah. "Sequential Monte Carlo methods for contour tracking of contaminant clouds." Signal Processing 90, no. 1 (January 2010): 249–60. http://dx.doi.org/10.1016/j.sigpro.2009.06.022.

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30

Liu, B., C. Ji, Y. Zhang, C. Hao, and K. K. Wong. "Multi-target tracking in clutter with sequential Monte Carlo methods." IET Radar, Sonar & Navigation 4, no. 5 (2010): 662. http://dx.doi.org/10.1049/iet-rsn.2009.0051.

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31

Sen, Deborshee, Ajay Jasra, and Yan Zhou. "Some contributions to sequential Monte Carlo methods for option pricing." Journal of Statistical Computation and Simulation 87, no. 4 (August 29, 2016): 733–52. http://dx.doi.org/10.1080/00949655.2016.1224238.

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32

Vogelstein, Joshua T., Brendon O. Watson, Adam M. Packer, Rafael Yuste, Bruno Jedynak, and Liam Paninski. "Spike Inference from Calcium Imaging Using Sequential Monte Carlo Methods." Biophysical Journal 97, no. 2 (July 2009): 636–55. http://dx.doi.org/10.1016/j.bpj.2008.08.005.

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33

Chen, Datong, and Jean-Marc Odobez. "Video text recognition using sequential Monte Carlo and error voting methods." Pattern Recognition Letters 26, no. 9 (July 2005): 1386–403. http://dx.doi.org/10.1016/j.patrec.2004.11.019.

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34

Creal, Drew. "A Survey of Sequential Monte Carlo Methods for Economics and Finance." Econometric Reviews 31, no. 3 (May 2012): 245–96. http://dx.doi.org/10.1080/07474938.2011.607333.

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35

Dettmer, Jan, Stan E. Dosso, and Charles W. Holland. "Sequential trans‐dimensional Monte Carlo methods for range‐dependent geoacoustic inversion." Journal of the Acoustical Society of America 129, no. 4 (April 2011): 2599. http://dx.doi.org/10.1121/1.3588617.

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36

Gove, J. H. "Propagating probability distributions of stand variables using sequential Monte Carlo methods." Forestry 82, no. 4 (May 23, 2009): 403–18. http://dx.doi.org/10.1093/forestry/cpp009.

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37

Xue, Haidong, Feng Gu, and Xiaolin Hu. "Data assimilation using sequential monte carlo methods in wildfire spread simulation." ACM Transactions on Modeling and Computer Simulation 22, no. 4 (November 2012): 1–25. http://dx.doi.org/10.1145/2379810.2379816.

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38

Hue, C., J. P. Le Cadre, and P. Perez. "Sequential Monte Carlo methods for multiple target tracking and data fusion." IEEE Transactions on Signal Processing 50, no. 2 (2002): 309–25. http://dx.doi.org/10.1109/78.978386.

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39

Beskos, Alexandros, Dan Crisan, and Ajay Jasra. "On the stability of sequential Monte Carlo methods in high dimensions." Annals of Applied Probability 24, no. 4 (August 2014): 1396–445. http://dx.doi.org/10.1214/13-aap951.

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40

Douc, Randal, Eric Moulines, and Jimmy Olsson. "Long-term stability of sequential Monte Carlo methods under verifiable conditions." Annals of Applied Probability 24, no. 5 (October 2014): 1767–802. http://dx.doi.org/10.1214/13-aap962.

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41

Wang, Liangliang, Shijia Wang, and Alexandre Bouchard-Côté. "An Annealed Sequential Monte Carlo Method for Bayesian Phylogenetics." Systematic Biology 69, no. 1 (June 6, 2019): 155–83. http://dx.doi.org/10.1093/sysbio/syz028.

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Abstract We describe an “embarrassingly parallel” method for Bayesian phylogenetic inference, annealed Sequential Monte Carlo (SMC), based on recent advances in the SMC literature such as adaptive determination of annealing parameters. The algorithm provides an approximate posterior distribution over trees and evolutionary parameters as well as an unbiased estimator for the marginal likelihood. This unbiasedness property can be used for the purpose of testing the correctness of posterior simulation software. We evaluate the performance of phylogenetic annealed SMC by reviewing and comparing with other computational Bayesian phylogenetic methods, in particular, different marginal likelihood estimation methods. Unlike previous SMC methods in phylogenetics, our annealed method can utilize standard Markov chain Monte Carlo (MCMC) tree moves and hence benefit from the large inventory of such moves available in the literature. Consequently, the annealed SMC method should be relatively easy to incorporate into existing phylogenetic software packages based on MCMC algorithms. We illustrate our method using simulation studies and real data analysis.
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42

Doshi, P., and P. J. Gmytrasiewicz. "Monte Carlo Sampling Methods for Approximating Interactive POMDPs." Journal of Artificial Intelligence Research 34 (March 24, 2009): 297–337. http://dx.doi.org/10.1613/jair.2630.

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Partially observable Markov decision processes (POMDPs) provide a principled framework for sequential planning in uncertain single agent settings. An extension of POMDPs to multiagent settings, called interactive POMDPs (I-POMDPs), replaces POMDP belief spaces with interactive hierarchical belief systems which represent an agent’s belief about the physical world, about beliefs of other agents, and about their beliefs about others’ beliefs. This modification makes the difficulties of obtaining solutions due to complexity of the belief and policy spaces even more acute. We describe a general method for obtaining approximate solutions of I-POMDPs based on particle filtering (PF). We introduce the interactive PF, which descends the levels of the interactive belief hierarchies and samples and propagates beliefs at each level. The interactive PF is able to mitigate the belief space complexity, but it does not address the policy space complexity. To mitigate the policy space complexity – sometimes also called the curse of history – we utilize a complementary method based on sampling likely observations while building the look ahead reachability tree. While this approach does not completely address the curse of history, it beats back the curse’s impact substantially. We provide experimental results and chart future work.
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43

Silva, João. "Definition of Maintenance Policies in Power Systems Using a Sequential Monte Carlo." U.Porto Journal of Engineering 1, no. 1 (September 6, 2017): 122–37. http://dx.doi.org/10.24840/2183-6493_001.001_0012.

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This paper reports an application of a simulationmethod called chronological Monte Carlo to evaluate power systems reliability. The Monte Carlo methods are, nowadays, the most widely used method for the estimation of reliability indices. Most of reliability studies that use Monte Carlo simulations are based on a hypothetical situation: the use of a constant failure rate ??. This paper demonstrates a new application that is able to include the typical variation of the failure rate ?? of electrical components that is represented by the well-known bathtub curve and, moreover, is able to show the advantages of different maintenance strategies. The results obtained with the Monte Carlo applications are compared with each other and with a typical Monte Carlo process. The proposed methodologies will be tested in the IEEE RTS-79.
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44

Brockwell, Anthony, Pierre Del Moral, and Arnaud Doucet. "Sequentially interacting Markov chain Monte Carlo methods." Annals of Statistics 38, no. 6 (December 2010): 3387–411. http://dx.doi.org/10.1214/09-aos747.

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45

Douc, Randal, and Eric Moulines. "Limit theorems for weighted samples with applications to sequential Monte Carlo methods." Annals of Statistics 36, no. 5 (October 2008): 2344–76. http://dx.doi.org/10.1214/07-aos514.

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46

Douc, R., and E. Moulines. "Limit theorems for weighted samples with applications to sequential Monte Carlo methods." ESAIM: Proceedings 19 (2007): 101–7. http://dx.doi.org/10.1051/proc:071913.

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47

Cappe, Olivier, Simon J. Godsill, and Eric Moulines. "An Overview of Existing Methods and Recent Advances in Sequential Monte Carlo." Proceedings of the IEEE 95, no. 5 (May 2007): 899–924. http://dx.doi.org/10.1109/jproc.2007.893250.

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48

Ba-Ngu Vo, S. Singh, and A. Boucet. "Sequential monte carlo methods for multi-target filtering with random finite sets." IEEE Transactions on Aerospace and Electronic Systems 41, no. 4 (October 2005): 1224–45. http://dx.doi.org/10.1109/taes.2005.1561884.

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49

Abu Znaid, Ammar M. A., Mohd Yamani Idna Idris, Ainuddin Wahid Abdul Wahab, Liana Khamis Qabajeh, and Omar Adil Mahdi. "Sequential Monte Carlo Localization Methods in Mobile Wireless Sensor Networks: A Review." Journal of Sensors 2017 (2017): 1–19. http://dx.doi.org/10.1155/2017/1430145.

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The advancement of digital technology has increased the deployment of wireless sensor networks (WSNs) in our daily life. However, locating sensor nodes is a challenging task in WSNs. Sensing data without an accurate location is worthless, especially in critical applications. The pioneering technique in range-free localization schemes is a sequential Monte Carlo (SMC) method, which utilizes network connectivity to estimate sensor location without additional hardware. This study presents a comprehensive survey of state-of-the-art SMC localization schemes. We present the schemes as a thematic taxonomy of localization operation in SMC. Moreover, the critical characteristics of each existing scheme are analyzed to identify its advantages and disadvantages. The similarities and differences of each scheme are investigated on the basis of significant parameters, namely, localization accuracy, computational cost, communication cost, and number of samples. We discuss the challenges and direction of the future research work for each parameter.
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50

Vergé, Christelle, Cyrille Dubarry, Pierre Del Moral, and Eric Moulines. "On parallel implementation of sequential Monte Carlo methods: the island particle model." Statistics and Computing 25, no. 2 (November 5, 2013): 243–60. http://dx.doi.org/10.1007/s11222-013-9429-x.

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