Comparison of Bayesian Methods for Recovering Sinusoids
INTERNATIONAL JOURNAL OF COMPUTERS AND COMMUNICATIONS, sa.15, ss.36-43, 2021 (Hakemli Dergi)
- Yayın Türü: Makale / Tam Makale
- Basım Tarihi: 2021
- Doi Numarası: 10.46300/91013.2021.15.7
- Dergi Adı: INTERNATIONAL JOURNAL OF COMPUTERS AND COMMUNICATIONS
- Derginin Tarandığı İndeksler: Index Copernicus, Other Indexes
- Sayfa Sayıları: ss.36-43
- Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
- İstanbul Üniversitesi Adresli: Evet
Özet
In this paper, we study a problem of estimating parameters of sinusoids from noisy data within Bayesian inferential framework. In this context, three different computational schemes such as, Bretthorst’s integral method (BRETTHORST), Gibbs sampling (GIBBS) and parallel tempering (PT) are studied and modifications of their algorithms were tested on data generated from synthetic signals. In addition, our emphasis is given to a comparison of their performances with respect to Cramér-Rao lower bound (CRLB)