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Hierarchical Graphical Bayesian Models in Psychology

Hierarchical graphical Bayesian models in psychology
Authors: Campitelli, Guillermo; Macbeth, Guillermo;

Hierarchical Graphical Bayesian Models in Psychology

Abstract

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.

Countries
Australia, Colombia, Argentina
Keywords

Graphical Models, Bayesian inference, Visual Statistics, modelos jerárquicos, Hierarchical Models, psychology, Bayesian statistics, 31 Colecciones de estadística general / Statistics, Modelos jerárquicos, cognición estadística, Hierarchical models, Cognición estadística, graphical models, StatisticalCognition, https://purl.org/becyt/ford/1.1, Psychology, Estadística visual, GraphicalModels, https://purl.org/becyt/ford/1, estadística Bayesiana, Estadística Bayesiana, visual statistics, Modelos gráficos, psicología, hierarchical models, Statistical Models, Statistics, Statistical Cognition, Psicología, HA1-4737, 51 Matemáticas / Mathematics, Visual statistics, statistical cognition, Statistical cognition, modelos gráficos, Graphical models, estadística visual, Bayesian Statistics, Applications of statistics to psychology

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    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3
Average
Average
Average
Green
gold