Abstract
Bayesian approach is a popular topic today in many fields of study in which statistics is used. The availability of stochastic simulation technique such as Markov Chain Monte Carlo makes exact Bayesian solution possible even in very complex and high dimensional models. The purpose of this short review paper is to emphasize the basic principles and to show the use of Markov Chain Monte Carlo technique for Chemistry data.
References
- A. Gelman, J.B. Carlin, S.H. Stern, D.B. Rubin, Bayesian data analysis, Chapman and Hall, 2004.
- D.B. Hibbert, N. Armstrong, An introduction to Bayesian methods for analyzing chemistry data: Part II: A review of applications of Bayesian methods in Chemistry, Chemometrics and intelligent laboratory systems, 97(2) (2009) 211.
- A. O’Hagan, Bayesian statistics: principles and benefits, Wageningen UR frontis series, Bayesian statistics and quality modeling in agro-food production chain, 3 (2004) 31.
- A. O’Hagan, Eliciting expert beliefs in substantial practical applications, The statistician, 47 (1998) 21.
- J.M. Bernardo, A.F.M. Smith, Bayesian theory, Wiley, 1994.
- G. Wright, P. Ayton, (eds.), Subjective probability, Wiley, 1994.
- The opportunities and advantages of using Bayesian statistics in clinical trials, http://www.tesella.com.
- D. Gamerman, Markov Chain Monte Carlo: Stochastic simulation for Bayesian inference, Chapman and Hall, 1997.
- B.P. Carlin, T.A. Louis, Bayesian methods for data analysis, CRC Press, 2008.
- A. Gelman, D.B. Rubin, Inference from iterative simulation using multiple sequences (with discussion), Statistical Science, 7 (1992) 457.
- S. Brooks, P. Giudici, MCMC convergence assessment via two way ANOVA, J. Computational and Graphical Statistics, 9 (2000) 266.
Abstract
Günümüzde Bayesci yaklaşım istatistiğin kullanıldığı birçok alanda revaçta olan bir yaklaşımdır. Stokastik simülasyon tekniği olan Markov Zinciri Monte Carlo yönteminin varlığı, karmaşık ve yüksek boyutlu modellerde bile Bayesci çözümlemelerin elde edilmesine olanak sağlar. Bu kısa derlemenin amacı, Bayesci yaklaşımın temel ilkeleri üzerinde durmak ve Markov Zinciri Monte Carlo yönteminin kimya verileri için nasıl kullanılabileceğini göstermektir
References
- A. Gelman, J.B. Carlin, S.H. Stern, D.B. Rubin, Bayesian data analysis, Chapman and Hall, 2004.
- D.B. Hibbert, N. Armstrong, An introduction to Bayesian methods for analyzing chemistry data: Part II: A review of applications of Bayesian methods in Chemistry, Chemometrics and intelligent laboratory systems, 97(2) (2009) 211.
- A. O’Hagan, Bayesian statistics: principles and benefits, Wageningen UR frontis series, Bayesian statistics and quality modeling in agro-food production chain, 3 (2004) 31.
- A. O’Hagan, Eliciting expert beliefs in substantial practical applications, The statistician, 47 (1998) 21.
- J.M. Bernardo, A.F.M. Smith, Bayesian theory, Wiley, 1994.
- G. Wright, P. Ayton, (eds.), Subjective probability, Wiley, 1994.
- The opportunities and advantages of using Bayesian statistics in clinical trials, http://www.tesella.com.
- D. Gamerman, Markov Chain Monte Carlo: Stochastic simulation for Bayesian inference, Chapman and Hall, 1997.
- B.P. Carlin, T.A. Louis, Bayesian methods for data analysis, CRC Press, 2008.
- A. Gelman, D.B. Rubin, Inference from iterative simulation using multiple sequences (with discussion), Statistical Science, 7 (1992) 457.
- S. Brooks, P. Giudici, MCMC convergence assessment via two way ANOVA, J. Computational and Graphical Statistics, 9 (2000) 266.
Details
| Primary Language | English |
|---|---|
| Authors | |
| Publication Date | January 1, 2011 |
| IZ | https://izlik.org/JA52LJ34MU |
| Published in Issue | Year 2011 Volume: 39 Issue: 1 |
Cite
HACETTEPE JOURNAL OF BIOLOGY AND CHEMİSTRY
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