A tutorial on bayesian networks for psychopathology researchers.
Psychol Methods
; 28(4): 947-961, 2023 Aug.
Article
en En
| MEDLINE
| ID: mdl-35113632
ABSTRACT
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).
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Modelos Estadísticos
/
Trastornos Mentales
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Psychol Methods
Asunto de la revista:
PSICOLOGIA
Año:
2023
Tipo del documento:
Article