Gibbs Sampling in Inference of Copula Gaussian Graphical Model Adapted to Biological Networks

• Authors: V. Purutçuoğlu , H. Farnoudkia
• Languages of publication: EN

Abstracts

EN

Markov chain Monte Carlo methods (MCMC) are iterative algorithms that are used in many Bayesian simulation studies, where the inference cannot be easily obtained directly through the defined model. Reversible jump MCMC methods belong to a special type of MCMC methods, in which the dimension of parameters can change in each iteration. In this study, we suggest Gibbs sampling in place of RJMCMC, to decrease the computational demand of the calculation of high dimensional systems. We evaluate the performance of the suggested algorithm in three real benchmark datasets, by comparing the accuracy and the computational demand with its strong alternatives, namely, birth-death MCMC, RJMCMC and QUIC algorithms. From the comparative analyses, we detect that Gibbs sampling improves the computational cost of RJMCMC without losing the accuracy.

Keywords

EN

02.50.-R; 02.50.-T

• Journal: Acta Physica Polonica A
• Year: 2017
• Volume: 132
• Issue: 3
• Pages: 1112-1117

Dates

published

2017-09

Contributors

author

V. Purutçuoğlu

• Middle East Technical University, Department of Statistics, Ankara, Turkey

author

H. Farnoudkia

• Middle East Technical University, Department of Statistics, Ankara, Turkey

References

• [1] Mazet and Brie, An alternative to the RJMCMC algorithm, Proceeding of IAR Annual Meeting, Nancy, 2006
• [2] P.J. Green, Biometrika 82, 711 (1995), doi: 10.1093/biomet/82.4.711
• [3] A. Dobra, A. Lenkoski, Ann. Appl. Statistics 5, 969 (2011), doi: 10.1214/10-AOAS397
• [4] A. Mohammadi, E. Wit, Int. Soc. Bayesian Anal. 10, 109 (2015)
• [5] S. Richardson, P.J. Green, J. Royal Stat. Soc. B 59, 731 (1997), doi: 10.1111/1467-9868.00095
• [6] Carlin, Chibs, J. Roy. Stat. Soc. B, 57, 473 (1995)
• [7] S. Walker, Electronic Journal of Statistics, arXiv: 0902.4117, 2009 http://arXiv.org/abs/0902.4117,
• [8] K. Ergen, A. Çıllı, N. Yahnıoğlu, Acta. Phys. Pol. A 128, B-273 (2015), doi: 10.12693/APhysPolA.128.B-273
• [9] M. Cevri, D. Üstündağ, Acta. Phys. Pol. A 130, 45 (2016), doi: 10.12693/APhysPolA.130.45
• [10] N. İyit, H. Yonar, A. Genç, Acta. Phys. Pol. A 130, 397 (2016), doi: 10.12693/APhysPolA.130.397
• [11] B. Muthen, Psychometrika 49, 115 (1984), doi: 10.1007/BF02294210
• [12] C.J. Hsieh, M.A. Sustik, I.S. Dhillon, P. Ravikumar, J. Machine Learning Res. 15, 2911 (2014)
• [13] J. Whittaker, Graphical Models in Applied Multivariate Statistics, John Wiley and Sons, 1990
• [14] K. Sachs, O. Perez, D. Pe'er, D.A. Lauenburger, G.P. Nolan, Science 308, 523 (2005), doi: 10.1126/science.1105809
• [15] V. Purutçuoğlu, E. Wit, Mathemat. Problems Engin. 2012, ID: 752631 (2012), doi: 10.1155/2012/752631
• [16] D. Gillespie, J. Phys. Chem. 81, 2340 (1977), doi: 10.1021/j100540a008
• [17] E.C. Brechmann, U. Schepsmeier, J. Statistical Software 52, 1 (2013), doi: 10.18637/jss.v052.i03
• [18] F. Abegaz, E. Wit, Biostatistics 14, 586 (2013), doi: 10.1093/biostatistics/kxt005
• [19] E. Boutalbi, L. Ait Gougam, F. Mekideche-Chafa, Acta. Phys. Pol. A 128, B-271 (2015), doi: 10.12693/APhysPolA.128.B-271
• [20] T. Aydogan, I. Ozcelik, I. Erturk, H. Ekiz, Acta. Phys. Pol. A 130, 412 (2016), doi: 10.12693/APhysPolA.130.412

Identifiers

DOI

10.12693/APhysPolA.132.1112