A note on parallel sampling in Markov graphs

Verena Bauer (), Karl Fürlinger and Göran Kauermann
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Verena Bauer: Ludwig-Maximilians-Universität München
Karl Fürlinger: Munich Network Management Team, Ludwig-Maximilians-Universität München
Göran Kauermann: Ludwig-Maximilians-Universität München

Computational Statistics, 2019, vol. 34, issue 3, No 8, 1087-1107

Abstract: Abstract The paper proposes the use of parallel computing for Markov graphs as a subclass of exponential random graph models where the network statistics induce a conditional independence structure amongst the edges of the network. This conditional independence allows simulation of edges in parallel using multiple computing cores. Simulation in Markov models is helpful, since parameter estimation cannot be carried out analytically but requires simulation-based routines such as Markov chain Monte Carlo. In particular in large networks this can be computationally very demanding or even infeasible. Therefore, numerical enhancements are useful to accelerate computation.

Keywords: Parallel computing; Network data; Exponential random graph model; Markov chain Monte Carlo (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s00180-019-00880-4

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