Hierarchical Graphical Bayesian Models in Psychology
The improvement of graphical methods in psychological research can promote their use and a better comprehension of their expressive power. The application of hierarchical Bayesian graphical models has recently become more frequent in psychological research. The aim of this contribution is to introdu...
- Autores:
-
Campitelli, Guillermo
Macbeth, Guillermo
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2014
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/66558
- Acceso en línea:
- https://repositorio.unal.edu.co/handle/unal/66558
http://bdigital.unal.edu.co/67586/
- Palabra clave:
- 51 Matemáticas / Mathematics
31 Colecciones de estadística general / Statistics
Visual Statistics
Graphical Models
Bayesian Statistics
Hierarchical Models
Psychology
Statistical Cognition
Cognición estadística
Estadística Bayesiana
Estadística visual
Modelos gráficos
Modelos jerárquicos
Psicología
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
| Summary: | The improvement of graphical methods in psychological research can promote their use and a better comprehension of their expressive power. The application of hierarchical Bayesian graphical models has recently become more frequent in psychological research. The aim of this contribution is to introduce suggestions for the improvement of hierarchical Bayesian graphical models in psychology. This novel set of suggestions stems from the description and comparison between two main approaches concerned with the use of plate notation and distribution pictograms. It is concluded that the combination of relevant aspects of both models might improve the use of powerful hierarchical Bayesian graphical models in psychology. |
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