ESTIMATING PARAMETERS OF SINUSOIDS FROM NOISY DATA USING MAXIMUM LIKELIHOOD TOGETHER WITH MONTE-CARLO SIMULATIONS
Year 2008,
Volume: 20 Issue: 1, 13 - 24, 25.11.2011
Dursun Üstündağ
,
Mehmet Cevri
Abstract
We consider here estimating parameters of signals from noisy data. For this purpose, a Mathematica program, in which an effective method based on the principle of maximum likelihood together with Monte-Carlo simulations is incorporated, was written and used for estimating the parameters of sinusoids corrupted by the Gaussian random noise.
References
- Bretthost, G. L.: “Bayesian Spectrum Analysis and Parameter Estimation”, Springer-Verlag, 12, (1997).
- Dou, L.; Hodgson, R.J.W.: “Bayesian Inference and Gibbs Sampling in Spectral Analysis and Parameter Estimation: I”, Inverse Problems, 11 (1995) 1069-1085.
- Andrieu, C.; Doucet, A.: “Joint Bayesian Model Selection and Estimation of Noisy Sinusoids via Reversible Jump MCMC”, IEEE Transactions on Signal Processing, Vol: 47, No: 10 (1999) 2667-2676.
- Capon, J.: “Maximum-likelihood spectral estimation”, Nonlinear Methods of Spectral Analysis, Springer-Verlag ( 1983).
- Gelman, A.; Carlin, J.B.; Stern, H.S.; Rubin, D.B.: “Bayesian Data Analysis”, Chapman & Hall/CRC, (2000).
- Cevri, M.: “Bayesian Parameter Estimation”, Yüksek Lisans Tezi, Marmara Üniv. Fen Bilimleri Enstitüsü, İstanbul, Türkiye, (2002).
- Fisher, R.A.: “Theory of Statistical Estimation”, Proceedings of the Cambridge Philosophy Society, 22 (1925), 700-725.
- Edwards, A.W.F.: “Likelihood”, Cambridge University Press, (1972).
- Press, W.H.; Flannery, B.P.; Teukolshy, S.A.; Vetterling, W.T.: “Numerical Recipes in C: The Art of Computing “, 2nd Ed.; Cambridge University Press, (1995).
- Kelly, J.J.: “Introduction to Data Analysis”, Essential Mathematica for Students of Science , (1998)
- Weillin, P.; Gayllord, R.; Kamin, S.: “An Introduction to Programming with Mathematica”, Cambridge University Press (2005).
- Tan, S.M.; Fox, C. ; Nicolls, G.K.: “Physics 707 Inverse Problems” (2006).
Ençok olabilirliğin monte-carlo simülasyonlarıyla birlikte kullanılmasıyla gürültülü verilerden sinüzoitlerin parametrelerinin kestirimi
Year 2008,
Volume: 20 Issue: 1, 13 - 24, 25.11.2011
Dursun Üstündağ
,
Mehmet Cevri
Abstract
Bu makalede gürültülü verilerden sinyallerin parametrelerinin kestirimini düşünüyoruz. Bu amaç için, Monte-Carlo simülasyonları ile birlikte en çok olabilirlik prensibinin kullanımına dayanan etkili bir yöntemin uygulandığı bir Mathematica programı yazıldı ve Gauss dağılımlı gürültülerle bozulmuş sinüzoitlerin parametrelerinin kestirimi için kullanıldı.
References
- Bretthost, G. L.: “Bayesian Spectrum Analysis and Parameter Estimation”, Springer-Verlag, 12, (1997).
- Dou, L.; Hodgson, R.J.W.: “Bayesian Inference and Gibbs Sampling in Spectral Analysis and Parameter Estimation: I”, Inverse Problems, 11 (1995) 1069-1085.
- Andrieu, C.; Doucet, A.: “Joint Bayesian Model Selection and Estimation of Noisy Sinusoids via Reversible Jump MCMC”, IEEE Transactions on Signal Processing, Vol: 47, No: 10 (1999) 2667-2676.
- Capon, J.: “Maximum-likelihood spectral estimation”, Nonlinear Methods of Spectral Analysis, Springer-Verlag ( 1983).
- Gelman, A.; Carlin, J.B.; Stern, H.S.; Rubin, D.B.: “Bayesian Data Analysis”, Chapman & Hall/CRC, (2000).
- Cevri, M.: “Bayesian Parameter Estimation”, Yüksek Lisans Tezi, Marmara Üniv. Fen Bilimleri Enstitüsü, İstanbul, Türkiye, (2002).
- Fisher, R.A.: “Theory of Statistical Estimation”, Proceedings of the Cambridge Philosophy Society, 22 (1925), 700-725.
- Edwards, A.W.F.: “Likelihood”, Cambridge University Press, (1972).
- Press, W.H.; Flannery, B.P.; Teukolshy, S.A.; Vetterling, W.T.: “Numerical Recipes in C: The Art of Computing “, 2nd Ed.; Cambridge University Press, (1995).
- Kelly, J.J.: “Introduction to Data Analysis”, Essential Mathematica for Students of Science , (1998)
- Weillin, P.; Gayllord, R.; Kamin, S.: “An Introduction to Programming with Mathematica”, Cambridge University Press (2005).
- Tan, S.M.; Fox, C. ; Nicolls, G.K.: “Physics 707 Inverse Problems” (2006).