A tutorial on bayesian networks for psychopathology researchers.

[en] 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) 2022 APA, all rights reserved).

Disciplines :

Neurosciences & behavior

Author, co-author :

Briganti, Giovanni ; Université de Mons - UMONS > Faculté de Médecine et de Pharmacie > Service de Neurosciences ; ORCID: 0000-0002-4038-3363 ; Department of Psychology.

Scutari, Marco; Istituto Dalle Molle di Studi sull'Intelligenza Artificiale (IDSIA).

McNally, Richard J; Department of Psychology.

Language :

English

Title :

A tutorial on bayesian networks for psychopathology researchers.

Publication date :

03 February 2022

Journal title :

Psychological methods

eISSN :

1939-1463

Peer reviewed :

Peer Reviewed verified by ORBi

Research institute :

Biosciences

Available on ORBi UMONS :

since 16 December 2022

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