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

详细信息    查看全文

• 作者：Pavel N. Krivitsky ^{pavel@uow.edu.au}
• 关键词：Curved exponential family ; ERGM ; Network data ; Partial stepping
• 刊名：Computational Statistics & Data Analysis
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：107
• 期：Complete
• 页码：149-161
• 全文大小：609 K
• 卷排序：107

文摘

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.Contrastive divergence is a more recent simulation-based approximation technique that uses a series of abridged MCMC runs instead of running them to stationarity. Combining it with the importance sampling Monte Carlo MLE yields a method for obtaining adequate initial values that is applicable to a wide variety of modeling scenarios. Practical issues such as stopping criteria and selection of tuning parameters are also addressed. A simple generalization of the Monte Carlo MLE partial stepping algorithm to curved exponential families (applicable to MLE-finding as well) is also proposed.The proposed approach reuses the aspects of an MLE implementation that are model-specific, so little to no additional implementer effort is required to obtain adequate initial parameters. This is demonstrated on a series of network datasets and models drawn from exponential-family random graph model computation literature, also exploring the limitations of the techniques considered.