| Autor: |
Briganti G; Department of Psychology, Harvard University., Scutari M; Istituto Dalle Molle di Studi sull'Intelligenza Artificiale (IDSIA)., McNally RJ; Department of Psychology, Harvard University. |
| Jazyk: |
angličtina |
| Zdroj: |
Psychological methods [Psychol Methods] 2023 Aug; Vol. 28 (4), pp. 947-961. Date of Electronic Publication: 2022 Feb 03. |
| DOI: |
10.1037/met0000479 |
| Abstrakt: |
Bayesian Networks are probabilistic graphical models that represent conditional independence relationships among variables as a directed acyclic graph (DAG), where edges can be interpreted as causal effects connecting one causal symptom to an effect symptom. These models can help overcome one of the key limitations of partial correlation networks whose edges are undirected. This tutorial aims to introduce Bayesian Networks to identify admissible causal relationships in cross-sectional data, as well as how to estimate these models in R through three algorithm families with an empirical example data set of depressive symptoms. In addition, we discuss common problems and questions related to Bayesian networks. We recommend Bayesian networks be investigated to gain causal insight in psychological data. (PsycInfo Database Record (c) 2023 APA, all rights reserved). |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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