Research Information System
international journal of applied mathematics and informatics, no.4, pp.123-134, 2013 (Peer-Reviewed Journal)
This paper deals with parameter estimation of sinusoids within a Bayesian framework, where inferences about parameters require an evaluation of complicated high dimensional integrals or a solution of multi-dimensional optimization. Unfortunately, it is not possible in general to derive analytical Bayesian inferences. Therefore, the purpose of this paper is to study some of existing stochastic procedures, based on different sampling schemes and to compare their performances with respect to Cramér-Rao lower bound (CRLB), defined to be a limit on the best possible performance achievable for a method given a dataset. Furthermore, all simulations support their effectiveness and demonstrate their performances in terms of CRLB for different lengths of data sampling and signal-to noise ratio (SNR) conditions.