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Results: 412
Number of items: 412
  • Open Access
    Gronau, Q. F., & Wagenmakers, E.-J. (2019). Rejoinder: More Limitations of Bayesian Leave-One-Out Cross-Validation. Computational Brain & Behavior, 2(1), 35-47. https://doi.org/10.1007/s42113-018-0022-4
  • Open Access
    van Doorn, J., Ly, A., Marsman, M., & Wagenmakers, E.-J. (2019). Bayesian estimation of Kendall’s τ using a latent normal approach. Statistics and Probability Letters, 145, 268-272. https://doi.org/10.1016/j.spl.2018.10.004
  • Sampaio, C., Ware, J. J., Macleod, M., Wagenmakers, E.-J., & Munafò, M. (2019). Reader response: Evaluating depression and suicidality in tetrabenazine users with Huntington disease. Neurology, 92(9), 446-448. https://doi.org/10.1212/WNL.0000000000006999
  • Open Access
    Haaf, J. M., Ly, A., & Wagenmakers, E.-J. (2019). Retire significance, but still test hypotheses. Nature, 567(7749), 461. https://doi.org/10.1038/d41586-019-00972-7
  • Open Access
    Stefan, A. M., Gronau, Q. F., Schönbrodt, F. D., & Wagenmakers, E.-J. (2019). A tutorial on Bayes Factor Design Analysis using an informed prior. Behavior Research Methods, 51(3), 1042-1058. https://doi.org/10.3758/s13428-018-01189-8
  • Open Access
    Gronau, Q. F., Wagenmakers, E.-J., Heck, D. W., & Matzke, D. (2019). A simple method for comparing complex models: Bayesian model comparison for hierarchical multinomial processing tree models using Warp-III bridge sampling. Psychometrika, 84(1), 261-284. https://doi.org/10.1007/s11336-018-9648-3
  • Open Access
    Gronau, Q. F., & Wagenmakers, E.-J. (2019). Limitations of Bayesian Leave-One-Out Cross-Validation for Model Selection. Computational Brain & Behavior, 2(1), 1-11. https://doi.org/10.1007/s42113-018-0011-7
  • Open Access
    Marsman, M., Waldorp, L., Dablander, F., & Wagenmakers, E.-J. (2019). Bayesian estimation of explained variance in ANOVA designs. Statistica Neerlandica, 73(3), 351-372. https://doi.org/10.1111/stan.12173
  • Open Access
    Evans, N. J., Tillman, G., & Wagenmakers, E.-J. (2019). Systematic and random sources of variability in perceptual decision-making: Comment on Ratcliff, Voskuilen, and McKoon (2018). (v1 ed.) PsyArXiv. https://doi.org/10.31234/osf.io/j98qd
  • Open Access
    Heck, D. W., Overstall, A. M., Gronau, Q. F., & Wagenmakers, E.-J. (2019). Quantifying uncertainty in transdimensional Markov chain Monte Carlo using discrete Markov models. Statistics and Computing, 29(4), 631-643. https://doi.org/10.1007/s11222-018-9828-0
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