| Full text | |
| Author(s): |
Total Authors: 3
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| Affiliation: | [1] Univ Fed Sao Carlos, Dept Stat, Sao Carlos - Brazil
[2] Univ Lyon, Ecole Normale Super Lyon, Lab Informat Parallelisme, Unite Math Pures & Appl, Lyon - France
[3] Univ Sao Paulo, Dept Stat, Inst Matemat & Estat, Sao Paulo - Brazil
Total Affiliations: 3
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| Document type: | Journal article |
| Source: | ESAIM-PROBABILITY AND STATISTICS; v. 24, p. 138-147, MAR 3 2020. |
| Web of Science Citations: | 0 |
| Abstract | |
In this paper, we propose a perfect simulation algorithm for the Exponential Random Graph Model, based on the Coupling from the past method of Propp and Wilson (1996). We use a Glauber dynamics to construct the Markov Chain and we prove the monotonicity of the ERGM for a subset of the parametric space. We also obtain an upper bound on the running time of the algorithm that depends on the mixing time of the Markov chain. (AU) | |
| FAPESP's process: | 19/17734-3 - Model selection in high dimensions: theoretical properties and applications |
| Grantee: | Florencia Graciela Leonardi |
| Support Opportunities: | Research Grants - eScience and Data Science Program - Regular Program Grants |
| FAPESP's process: | 13/07699-0 - Research, Innovation and Dissemination Center for Neuromathematics - NeuroMat |
| Grantee: | Oswaldo Baffa Filho |
| Support Opportunities: | Research Grants - Research, Innovation and Dissemination Centers - RIDC |
| FAPESP's process: | 15/12595-4 - Perfect simulation of Markov random fields on graphs |
| Grantee: | Andressa Cerqueira |
| Support Opportunities: | Scholarships abroad - Research Internship - Doctorate |