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Convergence of a Belief Propagation Algorithm for Biological Networks
- Source :
- Discrete Dynamics in Nature and Society, Vol 2019 (2019)
- Publication Year :
- 2019
- Publisher :
- Hindawi Limited, 2019.
-
Abstract
- Constructing network models of biological systems is important for effective understanding and control of the biological systems. For the construction of biological networks, a stochastic approach for link weights has been recently developed by using experimental data and belief propagation on a factor graph. The link weights were variable nodes of the factor graph and determined from their marginal probability mass functions which were approximated by using an iterative scheme. However, there is no convergence analysis of the iterative scheme. In this paper, at first, we present a detailed explanation of the complicated multistep process step by step with a network of small size and artificial experimental data, and then we show a sufficient condition for the convergence of the iterative scheme. Numerical examples are given to illustrate the whole process and to verify our result.
- Subjects :
- Mathematics
QA1-939
Subjects
Details
- Language :
- English
- ISSN :
- 10260226 and 1607887X
- Volume :
- 2019
- Database :
- Directory of Open Access Journals
- Journal :
- Discrete Dynamics in Nature and Society
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.412b0bc203d3421ca402e3c28fc14960
- Document Type :
- article
- Full Text :
- https://doi.org/10.1155/2019/9362179