University of Amsterdam, Psychological Methods, Nieuwe Achtergracht 129B, PO Box 15906, 1001 NK, Amsterdam, The Netherlands.
Centre for Urban Mental Health, Amsterdam, The Netherlands.
Psychometrika. 2022 Mar;87(1):47-82. doi: 10.1007/s11336-022-09848-8. Epub 2022 Feb 22.
The Ising model is one of the most widely analyzed graphical models in network psychometrics. However, popular approaches to parameter estimation and structure selection for the Ising model cannot naturally express uncertainty about the estimated parameters or selected structures. To address this issue, this paper offers an objective Bayesian approach to parameter estimation and structure selection for the Ising model. Our methods build on a continuous spike-and-slab approach. We show that our methods consistently select the correct structure and provide a new objective method to set the spike-and-slab hyperparameters. To circumvent the exploration of the complete structure space, which is too large in practical situations, we propose a novel approach that first screens for promising edges and then only explore the space instantiated by these edges. We apply our proposed methods to estimate the network of depression and alcohol use disorder symptoms from symptom scores of over 26,000 subjects.
Ising 模型是网络心理计量学中分析最多的图形模型之一。然而，流行的 Ising 模型参数估计和结构选择方法不能自然地表达对估计参数或选择结构的不确定性。为了解决这个问题，本文提出了一种针对 Ising 模型的参数估计和结构选择的客观贝叶斯方法。我们的方法建立在连续尖峰-板条方法的基础上。我们表明，我们的方法始终选择正确的结构，并提供了一种新的客观方法来设置尖峰-板条超参数。为了避免在实际情况下过于庞大的完整结构空间的探索，我们提出了一种新的方法，该方法首先筛选有希望的边缘，然后仅探索由这些边缘实例化的空间。我们将提出的方法应用于从超过 26000 名受试者的症状评分中估计抑郁和酒精使用障碍症状的网络。
