ArticlePsychometrika2022
Objective Bayesian Edge Screening and Structure Selection for Ising Networks.
Article in Psychometrika, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed.
- Exploring interrelationships between cognition, functioning and quality of life in schizophrenia spectrum disorders: a Bayesian analysis of networks.European archives of psychiatry and clinical neuroscience · 2026Article
- The impact of dichotomization on network recovery.Behavior research methods · 2025Article
- Bayes Factor Tests for Group Differences in Ordinal and Binary Graphical Models.Psychometrika · 2025Article
- Tell me why: A scoping review on the fundamental building blocks of fMRI-based network analysis.NeuroImage. Clinical · 2025Article
- A Good check on the Bayes factor.Behavior research methods · 2024Article
- A Note on Ising Network Analysis with Missing Data.Psychometrika · 2024Article
- Network analysis: An overview for mental health research.International journal of methods in psychiatric research · 2024Review
- Learning Bayesian Networks: A Copula Approach for Mixed-Type Data.Psychometrika · 2024Article
- Estimation of Personal Symptom Networks Using the Ising Model for Adult Survivors of Childhood Cancer: A Simulation Study with Real-World Data Application.Clinical epidemiology · 2024Article
- Guest Editors' Introduction to The Special Issue "Network Psychometrics in Action": Methodological Innovations Inspired by Empirical Problems.Psychometrika · 2022Article
- Possible Futures for Network Psychometrics.Psychometrika · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.