74 citations as recorded by crossref.

1. A New Evolutionary Particle Filter for the Prevention of Sample Impoverishment . Seongkeun Park et al. 10.1109/TEVC.2008.2011729
2. PREDICTED PERFORMANCE OF ESTIMATED SEEPAGE ANALYSIS MODEL BY DATA ASSIMILATION BASED ON MEASUREMENT DATA OF SOIL COLUMN TEST S. ITO et al. 10.2208/jscejge.76.4_350
3. Adaptive approximation of higher order posterior statistics W. Lee 10.1016/j.jcp.2013.11.015
4. Bayesian updating of model parameters using adaptive Gaussian process regression and particle filter I. Yoshida et al. 10.1016/j.strusafe.2023.102328
5. Parameter identification for Cam-clay model in partial loading model tests using the particle filter T. Shuku et al. 10.1016/j.sandf.2012.02.006
6. A Localized Particle Filter for High-Dimensional Nonlinear Systems J. Poterjoy 10.1175/MWR-D-15-0163.1
7. PROPOSAL OF THE METHOD ESTIMATING SEEPAGE ANALYSIS MODEL INCLUDING A BOUNDARY CONDITION USING MERGING PARTICLE FILTER S. ITO et al. 10.2208/jscejge.76.1_52
8. Two-Stage Filtering for Joint State-Parameter Estimation N. Santitissadeekorn & C. Jones 10.1175/MWR-D-14-00176.1
9. ESTIMATION OF POSTERIOR PROBABILITY DENSITY DISTRIBUTIONS BY SOME FILTERING ALGORITHMS FOR OBSERVATION UPDATE T. SHUKU et al. 10.2208/jscejam.71.I_59
10. Hybrid three-dimensional variation and particle filtering for nonlinear systems H. Leng & J. Song 10.1088/1674-1056/22/3/030505
11. The statistical observation localized equivalent-weights particle filter in a simple nonlinear model Y. Zhao et al. 10.1007/s13131-021-1876-1
12. Parameter estimation by ensemble Kalman filters with transformed data: Approach and application to hydraulic tomography A. Schöniger et al. 10.1029/2011WR010462
13. Particle filters for high‐dimensional geoscience applications: A review P. van Leeuwen et al. 10.1002/qj.3551
14. Physics-informed data assimilation model for displacement prediction of hydrodynamic pressure-driven landslide Y. Liu et al. 10.1016/j.compgeo.2024.106085
15. Beyond Gaussian Statistical Modeling in Geophysical Data Assimilation M. Bocquet et al. 10.1175/2010MWR3164.1
16. ESTIMATION OF BRIDGE DEFLECTION RESPONSE UNDER PASSING TRAIN LOADS BASED ON ACCELERATION K. MATSUOKA et al. 10.2208/jscejseee.69.527
17. Estimating cellular parameters through optimization procedures: elementary principles and applications A. Kimura et al. 10.3389/fphys.2015.00060
18. Estimating the parameters of neural mass models including time delay and nonlinearity using a particle filter: a preliminary study toward model‐based EEG analysis Y. Ogawa et al. 10.1002/tee.22481
19. Ensemble Riemannian data assimilation over the Wasserstein space S. Tamang et al. 10.5194/npg-28-295-2021
20. Nonlinear data assimilation in geosciences: an extremely efficient particle filter P. van Leeuwen 10.1002/qj.699
21. Identification of ship motion performance of coupled heave and pitch using experimental irregular time-series data T. Hanaki & M. Minoura 10.1007/s00773-022-00887-5
22. State-of-the-art stochastic data assimilation methods for high-dimensional non-Gaussian problems S. Vetra-Carvalho et al. 10.1080/16000870.2018.1445364
23. On Two Localized Particle Filter Methods for Lorenz 1963 and 1996 Models N. Schenk et al. 10.3389/fams.2022.920186
24. Bayesian Updating of Model Parameters by Iterative Particle Filter with Importance Sampling I. Yoshida & T. Shuku 10.1061/AJRUA6.0001047
25. Simultaneous state–parameter estimation of rainfall-induced landslide displacement using data assimilation J. Wang et al. 10.5194/nhess-19-1387-2019
26. Dual-particle-filtering for Recursive Estimation of Agricultural-machinery Dynamics M. Cooper & T. Perez 10.1016/j.ifacol.2018.09.210
27. A coherent structure approach for parameter estimation in Lagrangian Data Assimilation J. Maclean et al. 10.1016/j.physd.2017.08.007
28. Ensemble data assimilation for earthquake sequences: probabilistic estimation and forecasting of fault stresses Y. van Dinther et al. 10.1093/gji/ggz063
29. Numerical simulation of real-world flows T. Hayase 10.1088/0169-5983/47/5/051201
30. Data assimilation using the particle filter for identifying the elasto‐plastic material properties of geomaterials A. Murakami et al. 10.1002/nag.2125
31. Comparison and combination of EAKF and SIR-PF in the Bayesian filter framework Z. Shen et al. 10.1007/s13131-015-0757-x
32. A new formulation of vector weights in localized particle filters Z. Shen et al. 10.1002/qj.3180
33. Seepage analysis model based on field measurement data for estimation of posterior parameter distribution using merging particle filter S. Ito et al. 10.1016/j.sandf.2024.101442
34. Efficient nonlinear data-assimilation in geophysical fluid dynamics P. van Leeuwen 10.1016/j.compfluid.2010.11.011
35. Marginalized Particle Filtering Framework for Tuning of Ensemble Filters V. Šmídl & R. Hofman 10.1175/2011MWR3586.1
36. Technical Note: Sequential ensemble data assimilation in convergent and divergent systems H. Bauser et al. 10.5194/hess-25-3319-2021
37. Particle filter for model updating and reliability estimation of existing structures I. Yoshida & M. Akiyama 10.12989/sss.2013.11.1.103
38. METHOD FOR ESTIMATING SEEPAGE ANALYSIS MODEL INCLUDING BOUNDARY CONDITION USING MERGING PARTICLE FILTER S. ITO et al. 10.2208/journalofjsce.10.1_368
39. A non-Gaussian analysis scheme using rank histograms for ensemble data assimilation S. Metref et al. 10.5194/npg-21-869-2014
40. On Ensemble Kalman Filter, Particle Filter, and Gaussian Particle Filter M. Murata & K. Hiramatsu 10.5687/iscie.29.448
41. Particle Kalman Filtering: A Nonlinear Bayesian Framework for Ensemble Kalman Filters I. Hoteit et al. 10.1175/2011MWR3640.1
42. Estimation of a Long-Term Variation of a Magnetic-Storm Index Using the Merging Particle Filter S. NAKANO & T. HIGUCHI 10.1587/transinf.E92.D.1382
43. Efficient Kernel-Based Ensemble Gaussian Mixture Filtering B. Liu et al. 10.1175/MWR-D-14-00292.1
44. Modelling changes in travel behaviour mechanisms through a high-order hidden Markov model Z. Zhu et al. 10.1080/23249935.2022.2130731
45. Composing a surrogate observation operator for sequential data assimilation K. Akita et al. 10.14495/jsiaml.14.123
46. Data assimilation to determine the electrohydrodynamic force of plasma actuator Y. Kaneko et al. 10.1016/j.sna.2024.115320
47. Probabilistic calibration of discrete element simulations using the sequential quasi-Monte Carlo filter H. Cheng et al. 10.1007/s10035-017-0781-y
48. Comparison of sequential data assimilation methods for the Kuramoto–Sivashinsky equation M. Jardak et al. 10.1002/fld.2020
49. Linear versus Nonlinear Filtering with Scale-Selective Corrections for Balanced Dynamics in a Simple Atmospheric Model A. Subramanian et al. 10.1175/JAS-D-11-0332.1
50. Computational aspects of sequential Monte Carlo filter and smoother G. Kitagawa 10.1007/s10463-014-0446-0
51. Particle Filtering in Geophysical Systems P. van Leeuwen 10.1175/2009MWR2835.1
52. A sequential Bayesian approach for the estimation of the age–depth relationship of the Dome Fuji ice core S. Nakano et al. 10.5194/npg-23-31-2016
53. Exploring Practical Estimates of the Ensemble Size Necessary for Particle Filters L. Slivinski & C. Snyder 10.1175/MWR-D-14-00303.1
54. An Extension of the Ensemble Kalman Filter for Estimating the Observation Error Covariance Matrix Based on the Variational Bayes’s Method A. Nakabayashi & G. Ueno 10.1175/MWR-D-16-0139.1
55. Non-storm irregular variation of the <I>D</I><sub>st</sub> index S. Nakano & T. Higuchi 10.5194/angeo-30-153-2012
56. Obstacles to High-Dimensional Particle Filtering C. Snyder et al. 10.1175/2008MWR2529.1
57. International journal of computational fluid dynamics real-time prediction of unsteady flow based on POD reduced-order model and particle filter R. Kikuchi et al. 10.1080/10618562.2016.1198782
58. An iterative Bayesian filtering framework for fast and automated calibration of DEM models H. Cheng et al. 10.1016/j.cma.2019.01.027
59. A local particle filter for high-dimensional geophysical systems S. Penny & T. Miyoshi 10.5194/npg-23-391-2016
60. From the Ensemble Kalman Filter to the Particle Filter: a comparative study in rainfall-runoff models* D. Plaza et al. 10.3182/20110828-6-IT-1002.02326
61. Traffic state estimation on a two-dimensional network by a state-space model Y. Kawasaki et al. 10.1016/j.trc.2019.03.016
62. Application of Kalman Filter to Large-scale Geospatial Data: Modeling Population Dynamics H. Akatsuka & M. Terada 10.1145/3563692
63. Improved particle filter in data assimilation . Leng Hong-Ze et al. 10.7498/aps.61.070501
64. Key Points on The Reliability Engineering Y. NOMURA et al. 10.2472/jsms.65.403
65. Entropy Based Student’s t-Process Dynamical Model A. Nono et al. 10.3390/e23050560
66. Landslide displacement prediction based on time series analysis and data assimilation with hydrological factors J. Wang et al. 10.1007/s12517-020-05452-1
67. Data assimilation: Particle filter and artificial neural networks H. Furtado et al. 10.1088/1742-6596/135/1/012073
68. Estimation of Intracellular Calcium Ion Concentration by Nonlinear State Space Modeling and Expectation-Maximization Algorithm for Parameter Estimation T. Tsunoda et al. 10.1143/JPSJ.79.124801
69. Hydraulic parameter estimation by remotely-sensed top soil moisture observations with the particle filter C. Montzka et al. 10.1016/j.jhydrol.2011.01.020
70. A Moment Matching Ensemble Filter for Nonlinear Non-Gaussian Data Assimilation J. Lei & P. Bickel 10.1175/2011MWR3553.1
71. A surrogate-based approach to nonlinear, non-Gaussian joint state-parameter data assimilation J. Maclean & E. Spiller 10.3934/fods.2021019
72. Neural networks for emulation variational method for data assimilation in nonlinear dynamics H. Furtado et al. 10.1088/1742-6596/285/1/012036
73. A modified ensemble Kalman particle filter for non‐Gaussian systems with nonlinear measurement functions Z. Shen & Y. Tang 10.1002/2014MS000373
74. CONSTRUCTION OF ESTIMATION METHOD OF TRAFFIC FLOW RATE AT TRAFFIC ACCIDENT BY STATE SPACE MODEL Y. KAWASAKI et al. 10.2208/jscejipm.76.5_I_1297