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

Download

index.pdf

Date

2017-09-01

Author

Purutçuoğlu Gazi, Vilda

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

225
views

104
downloads

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.

Subject Keywords

Approximation

URI

https://hdl.handle.net/11511/37412

Journal

ACTA PHYSICA POLONICA A

DOI

https://doi.org/10.12693/aphyspola.132.1112

Collections

Department of Statistics, Article

Suggestions

OpenMETU
Core

Guaranteed Exponential Convergence without Persistent Excitation in Adaptive Control Yayla, Metehan; Kutay, Ali Türker (2016-09-02) In this paper, a new adaptive control framework for linear systems in which the matched uncertainty can be linearly parameterized is introduced to guarantee the global exponential stability of reference tracking error and parameter convergence error without requiring restrictive persistent excitation condition. The framework uses time histories of control input and system signals to construct least-squares problem based on recorded data. Then, unique solution to least-squares problem is computed, and assign...
Fuzzy Discrete Event Systems for Multiobjective Control: Framework and Application to Mobile Robot Navigation Schmidt, Klaus Verner (2012-10-01) Fuzzy discrete event systems (FDESs) have been introduced in recent years to model systems whose discrete states or discrete state transitions can be uncertain and are, hence, determined by a possibility degree. This paper develops an FDES framework for the control of sampled data systems that have to fulfill multiple objectives. The choice of a fuzzy system representation is justified by the assumption of a controller realization that depends on various potentially imprecise sensor measurements. The propos...
HAMILTON-JACOBI DYNAMICS FOR THE SOLUTION OF TIME-DEPENDENT QUANTUM PROBLEMS .1. FORMALISM AND WAVE-PACKET PROPAGATION IN ONE-DIMENSION YURTSEVER, E; BRICKMANN, J (1994-04-01) Two methods for the numerical integration of the time-dependent Schrodinger equation with given initial conditions (initial wave packet) are presented. The first method (method A) is based on the Schrodinger representation of the quantum-dynamical system while the second one (method B) is based upon the intermediate representation. In both cases the quantum dynamical equation is transformed into a system of Hamilton-Jacobi type equations of motion as occurring in multi particle classical dynamics, i.e. stan...
Analytical and numerical solutions to rotating variable thickness disks for a new thickness profile Eraslan, Ahmet Nedim (2015-09-01)
Analytical and Numerical Solutions to Rotating Variable Thickness Disks for a New Thickness Profile Eraslan, Ahmet Nedim (2014-09-28) Analytical and numerical solutions to rotating variable thickness disks are obtained. A new one-parameter exponential model is used to express the variation of disk thickness. It is shown by taking the limit that the present analytical solution reduces to the well-known homogeneous thickness solution. Furthermore, analytical and numerical solutions are brought into view together to allow comparison and further verification. The results of the solutions are presented in tables and figures to provide benchmar...

Citation Formats

V. Purutçuoğlu Gazi, “Gibbs Sampling in Inference of Copula Gaussian Graphical Model Adapted to Biological Networks,” ACTA PHYSICA POLONICA A, pp. 1112–1117, 2017, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/37412.