Using contrastive divergence to seed Monte Carlo MLE for exponential-family random graph models

Pavel N. Krivitsky

Computational Statistics & Data Analysis, 2017, vol. 107, issue C, 149-161

Abstract: Exponential-family models for dependent data have applications in a wide variety of areas, but the dependence often results in an intractable likelihood, requiring either analytic approximation or MCMC-based techniques to fit, the latter requiring an initial parameter configuration to seed their simulations. A poor initial configuration can lead to slow convergence or outright failure. The approximate techniques that could be used to find them tend not to be as general as the simulation-based and require implementation separate from that of the MLE-finding algorithm.

Keywords: Curved exponential family; ERGM; Network data; Partial stepping (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:107:y:2017:i:c:p:149-161

DOI: 10.1016/j.csda.2016.10.015

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