scholarly journals IMPROVEMENT ON ESTIMATION ACCURACY OF DIRECTIONAL SPECTRUM BY A BAYESIAN METHOD FOR SWELL OBSERVATION

2016 ◽  
Vol 72 (2) ◽  
pp. I_115-I_120
Author(s):  
Takashi FUJIKI ◽  
Noriaki HASHIMOTO ◽  
Koji KAWAGUCHI ◽  
Satoshi SAKURABA
Keyword(s):  
Bayesian Method ◽  
Estimation Accuracy ◽  
Directional Spectrum

scholarly journals VERIFICATION OF A MODIFIED BAYESIAN METHOD FOR ESTIMATING DIRECTIONAL WAVE SPECTRA FROM HF RADAR

2011 ◽  
Vol 1 (32) ◽  
pp. 65 ◽  
Author(s):  
Lukijanto Lukijanto ◽  
Noriaki Hashimoto ◽  
Masaru Yamashiro
Keyword(s):  
Field Data ◽  
Bayesian Method ◽  
Wave Spectrum ◽  
Coastal Engineering ◽  
Hf Radar ◽  
Ocean Wave ◽  
Wave Spectra ◽  
Directional Spectrum ◽  
Radio Science ◽  
Directional Wave

A Modified Bayesian Method (MBM) for estimating directional wave spectra from Doppler spectra obtained by HF radar is examined using field data which were employed in the verification of Bayesian Method (BM). Applicability, validity and accuracy of the MBM are demonstrated compared with the directional wave spectra estimated by BM and observed by buoy acquired from the reliable field data obtained from Surface Current and Wave Variability Experiments (SCAWVEX) project. The necessary conditions of the Doppler spectral components to be used to estimate a reliable directional spectrum are correspondingly estimated by BM. The results clearly demonstrate that directional wave spectra can be estimated by MBM on the basis of Doppler spectra. In addition, though BM shows very time consuming in computations, BM is more robust against the presence of noise than MBM. References Akaike, H. (1980). Likelihood and Bayesian procedure, Bayesian statistics. In J.M. Bernardo, M.H. De Groot, D.U. Lindley, and A.F.M. Smith (Eds.), 143-166. Valencia: University Press. PMid:6252024 Barrick, D. E. (1972a). First order theory and analysis of MF/HF/VHF scatter from sea. IEEE Trans., Antennas Propagation, 20, 2-10. http://dx.doi.org/10.1109/TAP.1972.1140123 Barrick, D. E. (1977). Extraction of wave parameters from measured HF radar sea-echo Doppler spectra. Radio Science, 12(3), 415–424. http://dx.doi.org/10.1029/RS012i003p00415 Crombie, D. (1955). Doppler spectrum of sea echo at 13.56Mc/s. Nature, 175, 681-682. http://dx.doi.org/10.1038/175681a0 Hashimoto, N. and Kobune, K. (1986). Estimation of directional spectra from the maximum entropy principle. Proceedings of 5th International Offshore Mechanics and Arctic Engineering Symposium, 1, 80-85. Hashimoto, N., Kobune, K., and Kameyama, Y. (1987). Estimation of directional spectrum using the Bayesian approach, and its application to field data analysis. Report of P.H.R.I., 26(5), 57-100. Hashimoto N., and Tokuda M., (1999): A Bayesian Method Approach for Estimation of Directional Wave Spectra with HF radar, Coastal Engineering Journal, vol. 41, 137-147. http://dx.doi.org/10.1142/S0578563499000097 Hashimoto, N., Wyatt, L and Kojima, S. (2003): Verification of Bayesian Method for Estimating Directional Spectra from HF Radar Surface. Coastal Engineering Journal, 45(2), 255-274. http://dx.doi.org/10.1142/S0578563403000725 Hashimoto, N., Lukijanto, and Yamashiro, M. (2008). Development of a practical method for estimating directional spectrum from HF radar backscatter. Annual Journal of Coastal Engineering (in Japanese), 55(1), 1451-1455. http://dx.doi.org/10.2208/proce1989.55.1451 Hisaki, Y. (1996). Nonlinear inversion of the integral equation to estimate ocean wave spectra from HF radar. Radio science, 31(1), 25-39. http://dx.doi.org/10.1029/95RS02439 Howell, R., and Walsh, J. (1993). Measurement of ocean wave spectra using a ship mounted HF radar. IEEE Journal of Oceanic Engineering, 18(3), 306-310. http://dx.doi.org/10.1109/JOE.1993.236369 Lipa, B. J. and Barrick, D.E. (1982) : Analysis Methods for Narrow-Beam High-Frequency Radar Sea Echo, NOAA Technical Report ERL 420-WPL 56, 1-55. Lukijanto, Hashimoto, N., and Yamashiro, M. (2009a). Further modification practical method for estimating directional wave spectrum by HF radar. Proc. of 19 th ISOPE, 898-905. Lukijanto, Hashimoto, N., and Yamashiro, M. (2009b). An improvement of Modified Bayesian Method for estimating directional wave spectra from HF radar backscatter. Proceedings of 5 th APAC (Asian and Pacific Coasts), 105-111. Lukijanto, Hashimoto, N., and Yamashiro, M. (2009c). A comparison of analysis methods for estimating directional wave spectrum from HF ocean radar. Journal of Memoirs of the Faculty of Engineering, 69(4). Kyushu University, 163-185. Wyatt, L.R. (1990). A relaxation method for integral inversion applied to HF radar measurement of the ocean wave directional spectrum. International Journal Remote Sensing, 11(8), 1481-1494. http://dx.doi.org/10.1080/01431169008955106 Wyatt, L. R. Gurgel, K.W., Peters, H.C., Prandle, D., Krogstad, H.E., Haug, O., Gerritsen, H., Wensink, G.J. (1997b). The SCAWVEX Project. Proceedings of WAVES97, ASCE.

scholarly journals A Bayesian Method for Dynamic Origin–Destination Demand Estimation Synthesizing Multiple Sources of Data

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 4971
Author(s):  
Hang Yu ◽  
Senlai Zhu ◽  
Jie Yang ◽  
Yuntao Guo ◽  
Tianpei Tang
Keyword(s):  
Posterior Distribution ◽  
Demand Uncertainty ◽  
Bayesian Method ◽  
Total Error ◽  
Demand Estimation ◽  
Estimation Accuracy ◽  
Multiple Sources ◽  
Traffic Counts ◽  
Traffic Count ◽  
Relative Errors

In this paper a Bayesian method is proposed to estimate dynamic origin–destination (O–D) demand. The proposed method can synthesize multiple sources of data collected by various sensors, including link counts, turning movements at intersections, flows, and travel times on partial paths. Time-dependent demand for each O–D pair at each departure time is assumed to satisfy the normal distribution. The connections among multiple sources of field data and O–D demands for all departure times are established by their variance-covariance matrices. Given the prior distribution of dynamic O–D demands, the posterior distribution is developed by updating the traffic count information. Then, based on the posterior distribution, both point estimation and the corresponding confidence intervals of O–D demand variables are estimated. Further, a stepwise algorithm that can avoid matrix inversion, in which traffic counts are updated one by one, is proposed. Finally, a numerical example is conducted on Nguyen–Dupuis network to demonstrate the effectiveness of the proposed Bayesian method and solution algorithm. Results show that the total O–D variance is decreasing with each added traffic count, implying that updating traffic counts reduces O–D demand uncertainty. Using the proposed method, both total error and source-specific errors between estimated and observed traffic counts decrease by iteration. Specifically, using 52 multiple sources of traffic counts, the relative errors of almost 50% traffic counts are less than 5%, the relative errors of 85% traffic counts are less than 10%, the total error between the estimated and “true” O–D demands is relatively small, and the O–D demand estimation accuracy can be improved by using more traffic counts. It concludes that the proposed Bayesian method can effectively synthesize multiple sources of data and estimate dynamic O–D demands with fine accuracy.

scholarly journals A Bayesian Approach for Estimating Causal Effects from Observational Data

2020 ◽  
Vol 34 (04) ◽  
pp. 5395-5402
Author(s):  
Johan Pensar ◽  
Topi Talvitie ◽  
Antti Hyttinen ◽  
Mikko Koivisto
Keyword(s):  
Bayesian Method ◽  
Causal Relation ◽  
Causal Effect ◽  
Small Sample ◽  
Causal Effects ◽  
Estimation Accuracy ◽  
Flow Cytometry Data ◽  
Mere Existence ◽  
Effect Estimation ◽  
Small Sample Sizes

We present a novel Bayesian method for the challenging task of estimating causal effects from passively observed data when the underlying causal DAG structure is unknown. To rigorously capture the inherent uncertainty associated with the estimate, our method builds a Bayesian posterior distribution of the linear causal effect, by integrating Bayesian linear regression and averaging over DAGs. For computing the exact posterior for all cause-effect variable pairs, we give an algorithm that runs in time O(3d d) for d variables, being feasible up to 20 variables. We also give a variant that computes the posterior probabilities of all pairwise ancestor relations within the same time complexity, significantly improving the fastest previous algorithm. In simulations, our Bayesian method outperforms previous methods in estimation accuracy, especially for small sample sizes. We further show that our method for effect estimation is well-adapted for detecting strong causal effects markedly deviating from zero, while our variant for computing posteriors of ancestor relations is the method of choice for detecting the mere existence of a causal relation. Finally, we apply our method on observational flow cytometry data, detecting several causal relations that concur with previous findings from experimental data.

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