Dynamic elicited priors for updating covert networks
Datos Bibliográficos
| ID | 7994094 |
|---|---|
| Autores | Jeff Gill (0000-0002-8592-8996, University of Washington), John R Freeman (University of Minnesota) |
| Año | 2013 |
| Volumen | 1 |
| Número | 1 |
| Páginas | 68-94 |
| Fecha de publicación | 2013-04-01 |
| Peer Reviewed | Sí |
| Open Access | Sí |
| Tipo | ARTICLE |
| Revista | Network Science (JOURNAL) |
| Identificadores de la revista | ISSN: 2050-1250 • E-ISSN: 2050-1242 |
| Editorial | Cambridge University Press (PUBLISHER • US) |
| DOI | 10.1017/nws.2012.6 |
| OpenAlex | W2125016258 |
| Idioma | EN |
| Citas recibidas | 4 |
| Referencias citadas | 86 |
The study of covert networks is plagued by the fact that individuals conceal their attributes and associations. To address this problem, we develop a technology for eliciting this information from qualitative subject-matter experts to inform statistical social network analysis. We show how the information from the subjective probability distributions can be used as input to Bayesian hierarchical models for network data. In the spirit of “proof of concept,” the results of a test of the technology are reported. Our findings show that human subjects can use the elicitation tool effectively, supplying attribute and edge information to update a network indicative of a covert one
Bayesian network · Bayesian probability · Covert · Data mining · Enhanced Data Rates for GSM Evolution · Machine learning · Prior probability · Computer Science · Crime Patterns and Interventions · Evolutionary Game Theory and Cooperation · Experimental Behavioral Economics Studies · Artificial Intelligence
Experts In Uncertainty
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Social Network Analysis
Estimation and Prediction for Stochastic Blockstructures
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Statistical Analysis With Missing Data
The Calculation of Posterior Distributions by Data Augmentation
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Optimal Predictions in Everyday Cognition
Inference and missing data
Judgment under Uncertainty
Inference from Iterative Simulation Using Multiple Sequences
Effects of missing data in social networks
Missing data in networks
The application of network analysis to criminal intelligence
The stability of centrality measures when networks are sampled
Bayesian Group Agents and Two Modes of Aggregation
A note on missing network data in the general social survey
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| Obras citantes distintas | 4 |
|---|---|
| Citas por año | 0,31 |
| Intervalo de citas | 2013 - 2026 (14) |
| Velocidad de citación | current |
| Altamente citado | No |
| Tipos de cita | Neutras: 4 |