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A note on perfect simulation for exponential random graph models

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arxiv 1710.00873 v1 pith:KZHFSDR5 submitted 2017-10-02 stat.CO

Andressa Cerqueira , Aurélien Garivier , Florencia Leonardi This is my paper · ORCID
classification stat.CO
keywords algorithmchainexponentialgraphmarkovperfectrandomsimulation
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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 & 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.

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  1. Graphical Construction of Spatial Gibbs Random Graphs

    math.ST 2019-08 conditional novelty 6.0 of 10

    For spatial exponential random graphs on Z^2, if the edge-length penalty is strong enough the finite-box Gibbs measures converge to a unique infinite-volume measure, which is exponentially mixing and satisfies a CLT, ...

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