https://scholars.lib.ntu.edu.tw/handle/123456789/611868
標題: | A Bayesian approach for the estimation of model parameters from noisy data sets | 作者: | Payne S.J. STEPHEN JOHN PAYNE |
關鍵字: | Algorithms;Convergence of numerical methods;Integration;Iterative methods;Mathematical models;Probability;Bayes procedure;Bayesian method;Probability models;Parameter estimation | 公開日期: | 2005 | 卷: | 12 | 期: | 8 | 起(迄)頁: | 553-556 | 來源出版物: | IEEE Signal Processing Letters | 摘要: | A Bayesian method is proposed for estimating model parameters from noisy data sets. The method is based on maximizing the posterior kernel, which enables priors on the model parameters to be incorporated. The posterior kernel is found by specifying hyperpriors and integrating the priors out, due to the use of conjugate priors. The use of probability models enables simultaneous data streams to be used to maximize the posterior kernel. The solution is found using an iterative scheme. The algorithm's performance is briefly illustrated using a real data set, demonstrating rapid convergence. ? 2005 IEEE. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-23944446845&doi=10.1109%2fLSP.2005.849542&partnerID=40&md5=35c4403b616d3c96035023ee60afd831 https://scholars.lib.ntu.edu.tw/handle/123456789/611868 |
DOI: | 10.1109/LSP.2005.849542 |
顯示於: | 應用力學研究所 |
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