2044-8317/asset/olbannerleft.png?v=1&s=8856da07bc63124271bd41692effae1b4eb4d01b)
2044-8317/asset/olbannerright.png?v=1&s=0e71c188d8e53b18773ec432a90707d049af0643)
Commentary
You have free access to this content
Posterior predictive checks can and should be Bayesian: Comment on Gelman and Shalizi, ‘Philosophy and the practice of Bayesian statistics’
Article first published online: 24 SEP 2012
DOI: 10.1111/j.2044-8317.2012.02063.x
© 2012 The British Psychological Society
Issue
British Journal of Mathematical and Statistical Psychology
Volume 66, Issue 1, pages 45–56, February 2013
Additional Information
How to Cite
Kruschke, J. K. (2013), Posterior predictive checks can and should be Bayesian: Comment on Gelman and Shalizi, ‘Philosophy and the practice of Bayesian statistics’. British Journal of Mathematical and Statistical Psychology, 66: 45–56. doi: 10.1111/j.2044-8317.2012.02063.x
Publication History
- Issue published online: 17 JAN 2013
- Article first published online: 24 SEP 2012
- Manuscript Received: 30 JAN 2012
- Manuscript Revised: 30 JAN 2012
References
- , , & (2006). Special issue: Probabilistic models of cognition. Trends in Cognitive Sciences, 10(7), 287–344.
- , & (1974). Theoretical statistics. London: Chapman & Hall.
- (2007). Technical note: How to use WinBUGS to draw inferences in animal models. Journal of Animal Science, 85, 1363–1368.
- , , , & (2004). Bayesian data analysis (2nd ed.). Boca Raton, FL: CRC Press.
- , & (2012). Philosophy and the practice of Bayesian statistics in the social sciences. In H. Kincaid (Ed.), The Oxford handbook of philosophy of social science. Oxford: Oxford University Press.Direct Link:
- , & (2013). Philosophy and the practice of Bayesian statistics. British Journal of Mathematical and Statistical Psychology, 66, 8–38. doi:10.1111/j.2044-8317.2011.02037.x
- , , , , & (2010). Probabilistic models of cognition: exploring representations and inductive biases. Trends in Cognitive Sciences, 14(8), 357–364.
- , & (2007). From mere coincidences to meaningful discoveries. Cognition, 103(2), 180–226.
- (1867). Handbuch der physiologischen Optik. Leipzig: L. Voss.
- , & (2010). Bayesian learning theory applied to human cognition. Wiley Interdisciplinary Reviews: Cognitive Science, 2, 8–21.
- , & (2003). A skew extension of the t-distribution, with applications. Journal of the Royal Statistical Society, Series B, 65(1), 159–174.
- , , & (2007). Iterated learning: Intergenerational knowledge transmission reveals inductive biases. Psychonomic Bulletin & Review, 14(2), 288–294.
- , , & (2004). Object perception as Bayesian inference. Annual Review of Psychology, 55, 271–304.
- , & (Eds.) (1996). Perception as Bayesian inference. Cambridge: Cambridge University Press.
- (2006). Locally Bayesian learning with applications to retrospective revaluation and highlighting. Psychological Review, 113(4), 677–699.
- (2010a). Bridging levels of analysis: comment on McClelland et al. and Griffiths et al. Trends in Cognitive Sciences, 14(8), 344–345.
- (2010b). What to believe: Bayesian methods for data analysis. Trends in Cognitive Sciences, 14(7), 293–300.
- (2011a). Bayesian assessment of null values via parameter estimation and model comparison. Perspectives on Psychological Science, 6(3), 299–312.
- (2011b). Doing Bayesian data analysis: A tutorial with R and BUGS. Burlington, MA: Academic Press/Elsevier.
- , , & (1989). Robust statistical modeling using the t distribution. Journal of the American Statistical Association, 84(408), 881–896.
- , & (2008). Bayes factors: Prior sensitivity and model generalizability. Journal of Mathematical Psychology, 52, 362–375.
- (2003). Information theory, inference & learning algorithms. Cambridge: Cambridge University Press.
- , & (2000). BUGS for a Bayesian analysis of stochastic volatility models. Econometrics Journal, 3(2), 198–215.Direct Link:
- (2003). JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling. Proceedings of the 3rd International Workshop on Distributed Statistical Computing (dsc 2003), Vienna.)
- R Development Core Team (2011). R: A language and environment for statistical computing [computer software manual]. Vienna: R Foundation for Statistical Computing. Retrieved from http://www.R-project.org
- , & (2010). Causal inference in perception. Trends in Cognitive Sciences, 14, 425–432.
- , , , & (2011). How to grow a mind: Statistics, structure, and abstraction. Science, 331(6022), 1279–1285.
- (2002). Bayesian inference in the noncentral Student-t model. Journal of Computational and Graphical Statistics, 11(1), 208–221.
- (2010). Prior sensitivity in theory testing: An apologia for the Bayes factor. Journal of Mathematical Psychology, 54, 491–498.
- (2008). Was Helmholtz a Bayesian? a review. Perception, 37, 642–650.
- , & (2008). Lacking control increases illusory pattern perception. Science, 322, 115–117.
- , & (2006). Vision as Bayesian inference: Analysis by synthesis? Trends in Cognitive Sciences, 10(7), 301–308.