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Journal articles on the topic 'Recursive Bayesian estimation'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 February 2022

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1

Kárný, Miroslav. "Approximate Bayesian recursive estimation." Information Sciences 285 (November 2014): 100–111. http://dx.doi.org/10.1016/j.ins.2014.01.048.

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2

Karlsson, R., and F. Gustafsson. "Recursive Bayesian estimation: bearings-only applications." IEE Proceedings - Radar, Sonar and Navigation 152, no. 5 (2005): 305. http://dx.doi.org/10.1049/ip-rsn:20045073.

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3

Thiemann, M., M. Trosset, H. Gupta, and S. Sorooshian. "Bayesian recursive parameter estimation for hydrologic models." Water Resources Research 37, no. 10 (October 2001): 2521–35. http://dx.doi.org/10.1029/2000wr900405.

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4

Jemmott, Colin W., and R. Lee Culver. "Recursive Bayesian state estimation for passive sonar localization." Journal of the Acoustical Society of America 127, no. 3 (March 2010): 1960. http://dx.doi.org/10.1121/1.3385007.

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5

Kramer, Stuart C., and Harold W. Sorenson. "Recursive Bayesian estimation using piece-wise constant approximations." Automatica 24, no. 6 (November 1988): 789–801. http://dx.doi.org/10.1016/0005-1098(88)90055-6.

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6

Vasudevan, Sathyanarayanan, Richard H. Anderson, Shawn Kraut, Peter Gerstoft, L. Ted Rogers, and Jeffrey L. Krolik. "Recursive Bayesian electromagnetic refractivity estimation from radar sea clutter." Radio Science 42, no. 2 (April 2007): n/a. http://dx.doi.org/10.1029/2005rs003423.

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7

Anyfantaki, Sofia, and Antonis Demos. "Estimation and Properties of a Time-Varying GQARCH(1,1)-M Model." Journal of Probability and Statistics 2011 (2011): 1–39. http://dx.doi.org/10.1155/2011/718647.

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Time-varying GARCH-M models are commonly used in econometrics and financial economics. Yet the recursive nature of the conditional variance makes exact likelihood analysis of these models computationally infeasible. This paper outlines the issues and suggests to employ a Markov chain Monte Carlo algorithm which allows the calculation of a classical estimator via the simulated EM algorithm or a simulated Bayesian solution in only computational operations, where is the sample size. Furthermore, the theoretical dynamic properties of a time-varying GQARCH(1,1)-M are derived. We discuss them and apply the suggested Bayesian estimation to three major stock markets.
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Odo, Wataru, Daisuke Kimoto, Makoto Kumon, and Tomonari Furukawa. "Active Sound Source Localization by Pinnae with Recursive Bayesian Estimation." Journal of Robotics and Mechatronics 29, no. 1 (February 20, 2017): 49–58. http://dx.doi.org/10.20965/jrm.2017.p0049.

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[abstFig src='/00290001/05.jpg' width='300' text='Schematic of the proposed system for actively localizing the sound source' ] Animals use two ears to localize the source of a sound, and this paper considers a robot system that localizes a sound source by using two microphones with active external reflectors that mimic movable pinnae. The body of the robot and the environment both affect the propagation of sound waves, which complicates mapping the acoustic cues to the source. The mapping may be multimodal, and the observed acoustic cues may lead to the incorrect estimation of the locations. In order to achieve sound source localization with such multimodal likelihoods, this paper presents a method for determining a configuration of active pinnae, which uses prior knowledge to optimize their location and orientation, and thus attenuates the effects of pseudo-peaks in the observations. The observations are also adversely affected by noise in the sensor signals, and thus Bayesian inference approach to process them is further introduced. Results of experiments that validate the proposed method are also presented.
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9

Tagade, Piyush, Krishnan S. Hariharan, Priya Gambhire, Subramanya Mayya Kolake, Taewon Song, Dukjin Oh, Taejung Yeo, and Seokgwang Doo. "Recursive Bayesian filtering framework for lithium-ion cell state estimation." Journal of Power Sources 306 (February 2016): 274–88. http://dx.doi.org/10.1016/j.jpowsour.2015.12.012.

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10

Chen, Yanbo, Feng Liu, Shengwei Mei, Guangyu He, Qiang Lu, and Yanlan Fu. "An Improved Recursive Bayesian Approach for Transformer Tap Position Estimation." IEEE Transactions on Power Systems 28, no. 3 (August 2013): 2830–41. http://dx.doi.org/10.1109/tpwrs.2013.2248761.

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11

Pavelková, Lenka, and Miroslav Kárný. "Approximate Bayesian Recursive Estimation of Linear Model with Uniform Noise*." IFAC Proceedings Volumes 45, no. 16 (July 2012): 1803–7. http://dx.doi.org/10.3182/20120711-3-be-2027.00104.

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12

Huang, Jianhui. "A recursive robust Bayesian estimation in partially observed financial market." Applicationes Mathematicae 34, no. 2 (2007): 237–52. http://dx.doi.org/10.4064/am34-2-8.

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13

al-Khateeb, Haider, Gregory Epiphaniou, Adam Reviczky, Petros Karadimas, and Hadi Heidari. "Proactive Threat Detection for Connected Cars Using Recursive Bayesian Estimation." IEEE Sensors Journal 18, no. 12 (June 15, 2018): 4822–31. http://dx.doi.org/10.1109/jsen.2017.2782751.

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14

Hanif, A., and P. Protopapas. "Recursive Bayesian estimation of regularized and irregular quasar light curves." Monthly Notices of the Royal Astronomical Society 448, no. 1 (February 5, 2015): 390–402. http://dx.doi.org/10.1093/mnras/stv004.

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15

Romero, Txomin, and Pedro Larrañaga. "Triangulation of Bayesian networks with recursive estimation of distribution algorithms." International Journal of Approximate Reasoning 50, no. 3 (March 2009): 472–84. http://dx.doi.org/10.1016/j.ijar.2008.09.002.

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16

Zhao, X. F., S. X. Huang, and D. X. Wang. "Using particle filter to track horizontal variations of atmospheric duct structure from radar sea clutter." Atmospheric Measurement Techniques 5, no. 11 (November 26, 2012): 2859–66. http://dx.doi.org/10.5194/amt-5-2859-2012.

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Abstract. This paper addresses the problem of estimating range-varying parameters of the height-dependent refractivity over the sea surface from radar sea clutter. In the forward simulation, the split-step Fourier parabolic equation (PE) is used to compute the radar clutter power in the complex refractive environments. Making use of the inherent Markovian structure of the split-step Fourier PE solution, the refractivity from clutter (RFC) problem is formulated within a nonlinear recursive Bayesian state estimation framework. Particle filter (PF), which is a technique for implementing a recursive Bayesian filter by Monte Carlo simulations, is used to track range-varying characteristics of the refractivity profiles. Basic ideas of employing PF to solve RFC problem are introduced. Both simulation and real data results are presented to confirm the feasibility of PF-RFC performances.
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17

Zhao, X. F., and S. X. Huang. "Using particle filter to track horizontal variations of atmospheric duct structure from radar sea clutter." Atmospheric Measurement Techniques Discussions 5, no. 4 (August 23, 2012): 6059–82. http://dx.doi.org/10.5194/amtd-5-6059-2012.

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Abstract. This paper addresses the problem of estimating range-varying parameters of the height-dependent refractivity over the sea surface from radar sea clutter. In the forward simulation, the split-step Fourier parabolic equation (PE) is used to compute the radar clutter power in the complex refractive environments. Making use of the inherent Markovian structure of the split-step Fourier PE solution, the refractivity from clutter (RFC) problem is formulated within a nonlinear recursive Bayesian state estimation framework. Particle filter (PF) that is a technique for implementing a recursive Bayesian filter by Monte Carlo simulations is used to track range-varying characteristics of the refractivity profiles. Basic ideas of employing PF to solve RFC problem are introduced. Both simulation and real data results are presented to check up the feasibility of PF-RFC performances.
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18

EL QALLI, YASSINE. "RECURSIVE BAYESIAN ESTIMATION IN FORWARD PRICE MODELS IMPLIED BY FAIR PRICING." International Journal of Theoretical and Applied Finance 13, no. 02 (March 2010): 301–33. http://dx.doi.org/10.1142/s0219024910005784.

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In this paper we describe a recursive Bayesian algorithm for the estimation of forward price models. The forward price is modeled within the benchmark framework for a forward price volatility function which includes a stochastic variable; a forward price with a liquidly traded maturity. A relationship between the bond price, the spot price and certain forward prices is stated. We set up the stochastic real world dynamics for these discretely compounded market observed forward prices. We propose a dynamic Bayesian estimation algorithm for a Monte Carlo time-discretized version of the resulting forward prices dynamics. The parameter to be estimated is a vector consisting of the forward price volatility parameters and the benchmarked bond price volatility parameters.
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19

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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20

Kyriazis, Gregory A., Márcio A. F. Martins, and Ricardo A. Kalid. "Bayesian recursive estimation of linear dynamic system states from measurement information." Measurement 45, no. 6 (July 2012): 1558–63. http://dx.doi.org/10.1016/j.measurement.2012.02.021.

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21

TERADA, Daisuke, and Toshio ISEKI. "Online Estimation of Directional Wave Spectra Based on Recursive Bayesian Modeling." Journal of Japan Institute of Navigation 108 (2003): 97–104. http://dx.doi.org/10.9749/jin.108.97.

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22

Xiang, Yijian, Murat Akcakaya, Satyabrata Sen, Deniz Erdogmus, and Arye Nehorai. "Target tracking via recursive Bayesian state estimation in cognitive radar networks." Signal Processing 155 (February 2019): 157–69. http://dx.doi.org/10.1016/j.sigpro.2018.09.035.

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23

Kárný, Miroslav, and Lenka Pavelková. "Projection-based Bayesian recursive estimation of ARX model with uniform innovations." Systems & Control Letters 56, no. 9-10 (September 2007): 646–55. http://dx.doi.org/10.1016/j.sysconle.2007.03.005.

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24

Kim, Jong-Hwan, and Seungsik Jo. "Recursive Bayesian Filter based Strike Velocity Estimation for Small Caliber Projectile." Journal of the Korea Institute of Military Science and Technology 19, no. 2 (April 5, 2016): 177–84. http://dx.doi.org/10.9766/kimst.2016.19.2.177.

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25

Rosen, Olov, and Alexander Medvedev. "The Recursive Bayesian Estimation Problem via Orthogonal Expansions: an Error Bound." IFAC Proceedings Volumes 47, no. 3 (2014): 5029–34. http://dx.doi.org/10.3182/20140824-6-za-1003.02195.

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26

Matoba, Chisato, Haruo Suemitsu, and Takami Matsuo. "Dual State-Parameter Estimation of ECG Signals with Recursive Bayesian Filters." Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications 2016 (2016): 55–60. http://dx.doi.org/10.5687/sss.2016.55.

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27

Riba, Jaume, and Gregori Vàzquez. "Bayesian recursive estimation of frequency and timing exploiting the cyclostationarity property." Signal Processing 40, no. 1 (October 1994): 21–37. http://dx.doi.org/10.1016/0165-1684(94)90019-1.

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28

Mehrara, Mohsen. "Return Predictability of Stock Price Index in Tehran Stock Exchange." International Letters of Social and Humanistic Sciences 9 (September 2013): 59–64. http://dx.doi.org/10.18052/www.scipress.com/ilshs.9.59.

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The question of whether asset price changes are predictable has long been the subject of many studies. Many studies, using historical returns based on random walk tests, have shown that stock return is not predictable. We study return predictability of the Tehran Exchange Price Index (TEPIX) based on monthly data from 2000 to 2011. For forecasting the return, we used a recursive estimation method in which the parameter estimates were updated recursively in light of new weekly observations, and also its regressors were changed recursively according to the Schwarz Bayesian Criterion. The results show that the daily stock returns are not predictable using publicly available information.
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29

Brockwell, A. E., A. L. Rojas, and R. E. Kass. "Recursive Bayesian Decoding of Motor Cortical Signals by Particle Filtering." Journal of Neurophysiology 91, no. 4 (April 2004): 1899–907. http://dx.doi.org/10.1152/jn.00438.2003.

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The population vector (PV) algorithm and optimal linear estimation (OLE) have been used to reconstruct movement by combining signals from multiple neurons in the motor cortex. While these linear methods are effective, recursive Bayesian decoding schemes, which are nonlinear, can be more powerful when probability model assumptions are satisfied. We have implemented a recursive Bayesian algorithm for reconstructing hand movement from neurons in the motor cortex. The algorithm uses a recently developed numerical method known as “particle filtering” and follows the same general strategy as that used by Brown et al. to reconstruct the path of a foraging rat from hippocampal place cells. We investigated the method in a numerical simulation study in which neural firing rate was assumed to be positive, but otherwise a linear function of movement velocity, and preferred directions were not uniformly distributed. In terms of mean-squared error, the approach was ∼10 times more efficient than the PV algorithm and 5 times more efficient than OLE. Thus use of recursive Bayesian decoding can achieve the accuracy of the PV algorithm (or OLE) with ∼10 times (or 5 times) fewer neurons. The method was also used to reconstruct hand movement in an ellipse-drawing task from 258 cells in the ventral premotor cortex. Recursive Bayesian decoding was again more efficient than the PV and OLE methods, by factors of roughly seven and three, respectively.
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30

Goodall, E. A., and D. Sprevak. "A Bayesian estimation of the lactation curve of a dairy cow." Animal Science 40, no. 2 (April 1985): 189–93. http://dx.doi.org/10.1017/s0003356100025290.

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ABSTRACTA recursive procedure for the estimation of the lactation curve of a dairy cow, which allows the inclusion of prior information on the curve and which takes account of the correlation between successive observations, is described. The method is based on the Kalman filter. It was found to give accurate estimates of the total milk yield at early stages of lactation.
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31

Chen, Shuangrui, and Quansheng Yan. "Reliability Estimation of Existing Concrete Bridge Based on Bayesian Dynamic Model." Open Civil Engineering Journal 9, no. 1 (September 10, 2015): 698–704. http://dx.doi.org/10.2174/1874149501509010698.

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It is of great significance to timely and accurately forecast the safety state of the bridge as far as the maintenance is concerned. Bayesian forecasting is a method of deriving posterior distribution in accord with the sampling information and prior information, where real time online forecasting is realized by means of recursive algorithm and the stationary assumption. Bayesian dynamic linear model is created to forecast the reliability of the bridge on the basis of the observed stress information of a bridge structure. According to the observed information, the model created is a superposition of constant mean model and seasonal effect model. The analysis of a practical example illustrates that Bayesian dynamic linear modes can provide an accurate real time forecast of the reliability of the bridge
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32

Achkasov, A. V., O. Ja Kravets, and E. S. Podvalny. "Distributed systems estimators and packet delivery prediction based on recursive Bayesian estimation." Automation and Remote Control 75, no. 10 (October 2014): 1880–85. http://dx.doi.org/10.1134/s0005117914100154.

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33

Aach, T., and D. Kunz. "Bayesian motion estimation for temporally recursive noise reduction in X-ray fluoroscopy." Philips Journal of Research 51, no. 2 (January 1998): 231–51. http://dx.doi.org/10.1016/s0165-5817(98)00004-7.

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34

Tariq Khan and P. Ramuhalli. "A Recursive Bayesian Estimation Method for Solving Electromagnetic Nondestructive Evaluation Inverse Problems." IEEE Transactions on Magnetics 44, no. 7 (July 2008): 1845–55. http://dx.doi.org/10.1109/tmag.2008.921842.

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35

Zhao, Shunyi, Choon Ki Ahn, Peng Shi, Yuriy S. Shmaliy, and Fei Liu. "Bayesian State Estimation for Markovian Jump Systems: Employing Recursive Steps and Pseudocodes." IEEE Systems, Man, and Cybernetics Magazine 5, no. 2 (April 2019): 27–36. http://dx.doi.org/10.1109/msmc.2018.2882145.

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36

Pavelková, Lenka, and Ladislav Jirsa. "Recursive Bayesian estimation of autoregressive model with uniform noise using approximation by parallelotopes." International Journal of Adaptive Control and Signal Processing 31, no. 8 (March 2, 2017): 1184–92. http://dx.doi.org/10.1002/acs.2756.

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37

Chen, Xiaobo, Yanjun Wang, Ling Chen, and Jianyu Ji. "Multi-Vehicle Cooperative Target Tracking with Time-Varying Localization Uncertainty via Recursive Variational Bayesian Inference." Sensors 20, no. 22 (November 13, 2020): 6487. http://dx.doi.org/10.3390/s20226487.

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Cooperative target tracking by multiple vehicles connected through inter-vehicle communication is a promising way to improve the estimation of target state. The effectiveness of cooperative tracking closely depends on the accuracy of relative localization between host and cooperative vehicles. However, the localization signal usually provided by the satellite-based navigation system is rather susceptible to dynamic driving environment, thus influencing the effectiveness of cooperative tracking. In order to implement reliable cooperative tracking, especially when the statistical characteristic of the relative localization noise is time-varying and uncertain, this paper presents a recursive Bayesian framework which jointly estimates the state of the target and the cooperative vehicle as well as the localization noise parameter. An online variational Bayesian inference algorithm is further developed to achieve efficient recursive estimate. The simulation results verify that our proposed algorithm can effectively boost the accuracy of target tracking when the localization noise dynamically changes over time.
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38

Zhao, Shunyi, and Fei Liu. "Recursive Bayesian estimation for Markov jump linear systems with unknown mode‐dependent state delays." IET Signal Processing 7, no. 9 (December 2013): 911–19. http://dx.doi.org/10.1049/iet-spr.2013.0012.

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39

Matoušek, Jakub, Jindřich Duník, and Ondřej Straka. "Density Difference Grid Design in a Point-Mass Filter." Energies 13, no. 16 (August 6, 2020): 4080. http://dx.doi.org/10.3390/en13164080.

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The paper deals with the Bayesian state estimation of nonlinear stochastic dynamic systems. The stress is laid on the point-mass filter, solving the Bayesian recursive relations for the state estimate conditional density computation using the deterministic grid-based numerical integration method. In particular, the grid design is discussed and the novel density difference grid is proposed. The proposed grid design covers such regions of the state-space where the conditional density is significantly spatially varying, by the dense grid. In other regions, a sparse grid is used to keep the computational complexity low. The proposed grid design is thoroughly discussed, analyzed, and illustrated in a numerical study.
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40

Zhang, Jian, and Ling Shen. "Applied Technology in an Adaptive Particle Filter Based on Interval Estimation and KLD-Resampling." Advanced Materials Research 1014 (July 2014): 452–58. http://dx.doi.org/10.4028/www.scientific.net/amr.1014.452.

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Particle filter as a sequential Monte Carlo method is widely applied in stochastic sampling for state estimation in a recursive Bayesian filtering framework. The efficiency and accuracy of the particle filter depends on the number of particles and the relocating method. The automatic selection of sample size for a given task is therefore essential for reducing unnecessary computation and for optimal performance, especially when the posterior distribution greatly varies overtime. This paper presents an adaptive resampling method (IE_KLD_PF) based on interval estimation, and after interval estimating the expectation of the system states, the new algorithm adopts Kullback-Leibler distance (KLD) to determine the number of particles to resample from the interval and update the filter results by current observation information. Simulations are performed to show that the proposed filter can reduce the average number of samples significantly compared to the fixed sample size particle filter.
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41

England, P. D., and R. J. Verrall. "Predictive Distributions of Outstanding Liabilities in General Insurance." Annals of Actuarial Science 1, no. 2 (September 2006): 221–70. http://dx.doi.org/10.1017/s1748499500000142.

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ABSTRACTThis paper extends the methods introduced in England & Verrall (2002), and shows how predictive distributions of outstanding liabilities in general insurance can be obtained using bootstrap or Bayesian techniques for clearly defined statistical models. A general procedure for bootstrapping is described, by extending the methods introduced in England & Verrall (1999), England (2002) and Pinheiro et al. (2003). The analogous Bayesian estimation procedure is implemented using Markov-chain Monte Carlo methods, where the models are constructed as Bayesian generalised linear models using the approach described by Dellaportas & Smith (1993). In particular, this paper describes a way of obtaining a predictive distribution from recursive claims reserving models, including the well known model introduced by Mack (1993). Mack's model is useful, since it can be used with data sets which exhibit negative incremental amounts. The techniques are illustrated with examples, and the resulting predictive distributions from both the bootstrap and Bayesian methods are compared.
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42

Awe, Olushina Olawale, and Abosede Adedayo Adepoju. "Change-point detection in CO2 emission-energy consumption nexus using a recursive Bayesian estimation approach." Statistics in Transition New Series 21, no. 1 (2020): 123–36. http://dx.doi.org/10.21307/stattrans-2020-007.

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43

Singh, Ravindra, Efthymios Manitsas, Bikash C. Pal, and Goran Strbac. "A Recursive Bayesian Approach for Identification of Network Configuration Changes in Distribution System State Estimation." IEEE Transactions on Power Systems 25, no. 3 (August 2010): 1329–36. http://dx.doi.org/10.1109/tpwrs.2010.2040294.

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44

Achkasov, A. V., O. Ja Kravets, and E. S. Podvalny. "Retraction Note to: “Distributed systems estimators and packet delivery prediction based on recursive Bayesian estimation”." Automation and Remote Control 76, no. 8 (August 2015): 1515. http://dx.doi.org/10.1134/s0005117915080147.

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45

Hyun, Myung Han, Bumshik Lee, and Munchurl Kim. "A Frame-Level Constant Bit-Rate Control Using Recursive Bayesian Estimation for Versatile Video Coding." IEEE Access 8 (2020): 227255–69. http://dx.doi.org/10.1109/access.2020.3046043.

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46

Sanger, Terence D. "Bayesian Filtering of Myoelectric Signals." Journal of Neurophysiology 97, no. 2 (February 2007): 1839–45. http://dx.doi.org/10.1152/jn.00936.2006.

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Surface electromyography is used in research, to estimate the activity of muscle, in prosthetic design, to provide a control signal, and in biofeedback, to provide subjects with a visual or auditory indication of muscle contraction. Unfortunately, successful applications are limited by the variability in the signal and the consequent poor quality of estimates. I propose to use a nonlinear recursive filter based on Bayesian estimation. The desired filtered signal is modeled as a combined diffusion and jump process and the measured electromyographic (EMG) signal is modeled as a random process with a density in the exponential family and rate given by the desired signal. The rate is estimated on-line by calculating the full conditional density given all past measurements from a single electrode. The Bayesian estimate gives the filtered signal that best describes the observed EMG signal. This estimate yields results with very low short-time variability but also with the capability of very rapid response to change. The estimate approximates isometric joint torque with lower error and higher signal-to-noise ratio than current linear methods. Use of the nonlinear filter significantly reduces noise compared with current algorithms, and it may therefore permit more effective use of the EMG signal for prosthetic control, biofeedback, and neurophysiology research.
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47

Jiang, Haonan, and Yuanli Cai. "Adaptive Fifth-Degree Cubature Information Filter for Multi-Sensor Bearings-Only Tracking." Sensors 18, no. 10 (September 26, 2018): 3241. http://dx.doi.org/10.3390/s18103241.

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Standard Bayesian filtering algorithms only work well when the statistical properties of system noises are exactly known. However, this assumption is not always plausible in real target tracking applications. In this paper, we present a new estimation approach named adaptive fifth-degree cubature information filter (AFCIF) for multi-sensor bearings-only tracking (BOT) under the condition that the process noise follows zero-mean Gaussian distribution with unknown covariance. The novel algorithm is based on the fifth-degree cubature Kalman filter and it is constructed within the information filtering framework. With a sensor selection strategy developed using observability theory and a recursive process noise covariance estimation procedure derived using the covariance matching principle, the proposed filtering algorithm demonstrates better estimation accuracy and filtering stability. Simulation results validate the superiority of the AFCIF.
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48

Furukawa, Tomonari, and John G. Michopoulos. "An Information-Theoretic Approach for Computational Material Modeling." Advanced Materials Research 33-37 (March 2008): 857–62. http://dx.doi.org/10.4028/www.scientific.net/amr.33-37.857.

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This paper presents an information-theoretic approach for computational material modeling, which characterizes materials by effectively utilizing all the known information including prior and empirical information. The approach is built within the framework of recursive Bayesian estimation where various inverse analysis techniques, such as Singular Value Decomposition (SVD) and Kalman Filter (KF) can be implemented. Numerical examples first investigate the validity of the proposed approach via parametric studies. The proposed approach has been then successfully applied to the identification of a composite specimen using a triaxial testing machine.
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49

H. Nguyen, Thanh, and Hung T. Nguyen. "A Bayesian Recursive Algorithm for Freespace Estimation Using a Stereoscopic Camera System in an Autonomous Wheelchair." American Journal of Biomedical Engineering 1, no. 1 (August 31, 2012): 44–54. http://dx.doi.org/10.5923/j.ajbe.20110101.08.

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Hotson, Guy, Ryan J. Smith, Adam G. Rouse, Marc H. Schieber, Nitish V. Thakor, and Brock A. Wester. "High Precision Neural Decoding of Complex Movement Trajectories Using Recursive Bayesian Estimation With Dynamic Movement Primitives." IEEE Robotics and Automation Letters 1, no. 2 (July 2016): 676–83. http://dx.doi.org/10.1109/lra.2016.2516590.

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