A Bayesian approach for the estimation of model parameters from noisy data sets

Payne S.J.STEPHEN JOHN PAYNE2022-05-242022-05-242005https://www.scopus.com/inward/record.uri?eid=2-s2.0-23944446845&doi=10.1109%2fLSP.2005.849542&partnerID=40&md5=35c4403b616d3c96035023ee60afd831https://scholars.lib.ntu.edu.tw/handle/123456789/611868A 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.AlgorithmsConvergence of numerical methodsIntegrationIterative methodsMathematical modelsProbabilityBayes procedureBayesian methodProbability modelsParameter estimationA Bayesian approach for the estimation of model parameters from noisy data setsjournal article10.1109/LSP.2005.8495422-s2.0-23944446845