Nonlinear Least Squares and Bayesian Inference

Advanced Mathematical and Computational Tools in Metrology and Testing VIII

Volumn , Issue , 2009, Pages 104-112

  Forbes, Alistair B ^a  

^a NATIONAL PHYSICAL LABORATORY   (United Kingdom)

Author keywords

Bayesian inference; Markov chain monte carlo; Parameter estimation; Uncertainty

Indexed keywords

EID: 85131465937     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1142/9789812839527_0016     Document Type: Chapter

References (8)

1. R. M. Barker, M. G. Cox, A. B. Forbes, and P. M. Harris. SSfM Best Prac-tice Guide No. 4: Modelling Discrete Data and Experimental Data Analysis. Report DEM-ES-018, National Physical Laboratory, Teddington, UK, 2007.
2. M. G. Cox and P. M. Harris. SSfM Best Practice Guide No. 6, Uncertainty evaluation. Report DEM-ES-011, National Physical Laboratory, Teddington, UK, 2006.
3. M. G. Cox, G. B. Rossi, P. M. Harris, and A. Forbes. A probabilistic approach to the analysis of measurement processes. Metrologia, 45(5): 493-502, 2008.
4. A. B. Forbes. Parameter estimation based on least-squares methods. In F. Pavese and A. B. Forbes, editors, Data modeling for metrology and testing in measurement science, New York. Birkauser-Boston. In press.
5. D. Gamerman. Markov chain Monte Carlo: Stochastic Simulation for Bayesian inference. Taylor & Francis, New York, 1997.
6. A. Gelman, J. B. Carlin, H. S. Stern, and D. B. Rubin. Bayesian Data Analysis. Chapman & Hall/CRC, Boca Raton, Fl., second edition, 2004.
7. G. H. Golub and C. F. Van Loan. Matrix Computations. John Hopkins University Press, Baltimore, third edition, 1996.
8. D. S. Sivia. Data Analysis: a Bayesian Tutorial. Clarendon Press, Oxford, 1996.