Hierarchical Graphical Bayesian Models in Psychology

• GUILLERMO CAMPITELLI ^[1] ; GUILLERMO MACBETH ^[2]
  1. [1] Edith Cowan University

     Edith Cowan University

     Australia

     
  2. [2] Universidad Nacional de Entre Ríos

     Universidad Nacional de Entre Ríos

     Argentina

     
• Localización: Revista Colombiana de Estadística, ISSN-e 2389-8976, ISSN 0120-1751, Vol. 37, Nº. 2, 2014, págs. 319-339
• Idioma: inglés
• DOI: 10.15446/rce.v37n2spe.47940
• Títulos paralelos:
  • Modelos Bayesianos gráficos jerárquicos en psicología
• Enlaces
  • Texto completo
• Resumen
  • español

    El mejoramiento de los métodos gráficos en la investigación en psicología puede promover su uso y una mejor compresión de su poder de expresión. La aplicación de modelos Bayesianos gráficos jerárquicos se ha vuelto más frecuente en la investigación en psicología. El objetivo de este trabajo es introducir sugerencias para el mejoramiento de los modelos Bayesianos gráficos jerárquicos en psicología. Este conjunto de sugerencias se apoya en la descripción y comparación entre los dos enfoques principales con el uso de notación y pictogramas de distribución. Se concluye que la combinación de los aspectos relevantes de ambos puede mejorar el uso de los modelos Bayesianos gráficos jerárquicos en psicología

  • English

    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.

• Referencias bibliográficas
  • Ahn, W.-Y.,Krawitz, A.,Kim, W.,Busemeyer, J. R.,Brown, J. W.. (2011). 'A model-based FMRI analysis with hierarchical Bayesian parameter...
  • Bauer, M. I.,Johnson-Laird, P. N.. (1993). 'How diagrams can improve reasoning'. Psychological Science. 4. 372-378
  • Beyth Marom, R.,Fidler, F.,Cumming, G.. (2008). 'Statistical cognition: towards evidence-based practice in statistics and statistics education'....
  • Bolstad, W. M.. (2007). Introduction to Bayesian Statistics. 2. Wiley Jobs in Hoboken.
  • Bridwell, D. A.,Hecker, E. A.,Serences, J. T.,Srinivasan, R.. (2013). 'Individual differences in attention strategies during detection,...
  • Buntine, W. L.. (1994). 'Operations for learning with graphical models'. Journal of Artificial Intelligence Research. 2. 159-225
  • (2013). Bååth, R.. Distribution diagram R scripts.
  • Dienes, Z.. (2011). 'Bayesian versus orthodox statistics: which side are you on?'. Perspectives on Psychological Science. 6. 274-290
  • Dyjas, O.,Grasman, R. P.,Wetzels, R.,van der Maas, H. L.,Wagenmakers, E.. (2012). 'A Bayesian hierarchical analysis of the name-letter...
  • Edwards, W.,Lindman, H.,Savage, L. J.. (1963). 'Bayesian statistical inference for psychological research'. Psychological Review....
  • Gilks, W. R.,Thomas, A.,Spiegelhalter, D. J.. (1994). 'A language and program for complex Bayesian modelling'. The Statistician. 43....
  • Good, I. J.. (1980). 'Some history of the hierarchical Bayesian methodology'. Trabajos de Estadística y de Investigación Operativa....
  • Gray, K.,Wegner, D. M.. (2013). 'Six guidelines for interesting research'. Perspectives on Psychological Science. 8. 549-553
  • Griffiths, T. L.,Kemp, C.,Tenenbaum, J. B.. (2008). 'The Cambridge Handbook of Computational Psychology'. Cambridge University Press....
  • Heit, E.,Rotello, C.. (2005). 'Proceedings of the 27th Annual Conference of the Cognitive Science Society'. Lawrence, Erlbaum. Mahwah....
  • Jordan, M. I.. (2004). 'Graphical models'. Statistical Science. 19. 140-155
  • Koller, D.,Friedman, N.. (2009). Probabilistic Graphical Models: Principles and Techniques. MIT press. Cambridge.
  • Koller, D.,Friedman, N.,Getoor, L.,Taskar, B.. (2007). 'Introduction to statistical relational learning'. MIT Press. Cambridge.
  • Kruschke, J. K.. (2010). Doing Bayesian Data Analysis: A Tutorial with R and BUGS. Academic Press. Burlington.
  • Kruschke, J. K.. (2010). 'What to believe: bayesian methods for data analysis'. Trends in Cognitive Science. 14. 293-300
  • Kruschke, J. K.. (2013). 'Bayesian estimation supersedes the t test'. Journal of Experimental Psychology: General. 142. 573-603
  • Kruschke, J. K.,Aguinis, H.,Joo, H.. (2012). 'The time has come: Bayesian methods for data analysis in the organizational sciences'....
  • Lee, M. D.. (2008). 'Three case studies in the Bayesian analysis of cognitive models'. Psychonomic Bulletin and Review. 15. 1-15
  • Lee, M. D.. (2011). 'How cognitive modeling can benefit from hierarchical Bayesian models'. Journal of Mathematical Psychology. 55....
  • Lee, M. D.,Newell, B. R.. (2011). 'Using hierarchical Bayesian methods to examine the tools of decision-making'. Judgment and Decision...
  • Lee, M. D.,Wagenmakers, E.-J.. (2005). 'Postscript: Bayesian statistical inference in psychology: Comment on Trafimow (2003)'. Psychological...
  • Lee, M. D.,Wagenmakers, E.-J.. (2014). 'Bayesian cognitive modeling: A practical course'. New York: Cambridge University Press.
  • Lodewyckx, T.,Kim, W.,Lee, M. D.,Tuerlinckx, F.,Kuppens, P.,Wagenmakers, E. J.. (2011). 'A tutorial on Bayes factor estimation with the...
  • Lunn, D. J.,Thomas, A.,Best, N.,Spiegelhalter, D.. (2000). 'A Bayesian modelling framework: Concepts, structure, and extensibility'....
  • Nosofsky, R. M.. (1984). 'Choice, similarity, and the context theory of classification'. Journal of Experimental Psychology: Learning,...
  • Nosofsky, R. M.. (1986). 'Attention, similarity, and the identification\textendashcategorization relationship'. Journal of Experimental...
  • Orhan, A. E.,Jacobs, R. A.. (2013). 'A probabilistic clustering theory of the organization of visual short-term memory'. Psychological...
  • Pearl, J.. (2009). 'Causal inference in statistics: an overview'. Statistics Surveys. 3. 96-146
  • (2013). R Development Core Team, R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Vienna.
  • Scheibehenne, B.,Rieskamp, J.,Wagenmakers, E.-J.. (2013). 'Testing adaptive toolbox models: A Bayesian hierarchical approach'. Psychological...
  • (2013). Schneider, T.. Diagram for Hierarchical Models.
  • Shepard, R. N.. (1962). 'The analysis of proximities: multidimensional scaling with an unknown distance function. I'. Psychometrika....
  • Shepard, R. N.. (1980). 'Multidimensional scaling, tree-fitting, and clustering'. Science. 210. 390-398
  • (1996). Spiegelhalter, D., Thomas, A., Best, N. & Gilks, W. BUGS 0.5* Examples Volume 2 (version ii). 2. MRC Biostatistics Unit.
  • Stenning, K.,Oberlander, J.. (1995). 'A cognitive theory of graphical and linguistic reasoning: logic and implementation'. Cognitive...
  • Steyvers, M.,Lee, M. D.,Wagenmakers, E.-J.. (2009). 'Bayesian analysis of human decision-making on bandit problems'. Journal of Mathematical...
  • Wagenmakers, E.. (2007). 'A practical solution to the pervasive problems of p values'. Psychonomic Bulletin and Review. 14. 779-804
  • Wickens, C. D.,Merwin, D. H.,Lin, E. L.. (1994). 'Implications of graphics enhancements for the visualization of scientific data: dimensional...
  • van Ravenzwaaij, D.,Moore, C. P.,Lee, M. D.,Newell, B. R.. (2014). 'A hierarchical Bayesian modeling approach to searching and stopping...