Abstract
In many practical applications of statistical signal processing, the likelihood functions are only partially known. The measurement model in this case is affected by two sources of uncertainty: stochastic uncertainty and imprecision. Following the framework of random set theory , the paper presents the optimal Bayesian estimator for this problem. The resulting Bayes estimator in general has no analytic closed form solution, but can be approximated, for example, using the Monte Carlo method. A numerical example is included to illustrate the theory.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.