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

100%

Purutçuoğlu V., Farnoudkia H.

Acta Physica Polonica A

2017

vol. 132

issue 3

1112-1117

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.

Recovering Images from PET Camera

70%

Üstündağ D.

Acta Physica Polonica A

2017

vol. 132

issue 3

963-966

We study here one of the imaging techniques, used in nuclear medicine, called positron emission tomographic imaging, that provides information about many biological processes that are essential to the functioning of the organ, being visualized. Our emphasis is given to the application of the maximum entropy image reconstruction method called "Cambridge MaxEnt Package" for recovering images of the human brain from data obtained by positron emission tomographic camera.

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2 Acta Physica Polonica A

2 2017

1 Farnoudkia H.

1 Purutçuoğlu V.

1 Üstündağ D.

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

Recovering Images from PET Camera