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

Abstract

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.