Search results

  • Full text

  • Document type

    • Publication year

  • Organisation

Results: 94
Number of items: 94
  • 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
    Waldorp, L., Marsman, M., & Maris, G. (2019). Logistic regression and Ising networks: Prediction and estimation when violating lasso assumptions. Behaviormetrika, 46(1), 49-72. https://doi.org/10.1007/s41237-018-0061-0
  • Open Access
    Savi, A. O., Marsman, M., van der Maas, H. L. J., & Maris, G. K. J. (2019). The Wiring of Intelligence. Perspectives on Psychological Science, 14(6), 1034-1061. https://doi.org/10.1177/1745691619866447
  • Ly, A., Böhm, U., Heathcote, A., Turner, B. M., Forstmann, B., Marsman, M., & Matzke, D. (2018). A flexible and efficient hierarchical Bayesian approach to the exploration of individual differences in cognitive-model-based neuroscience. In A. A. Moustafa (Ed.), Computational Models of Brain and Behavior (pp. 467-480). Wiley Blackwell. https://doi.org/10.1002/9781119159193.ch34
  • Open Access
    Kruis, J., Molenaar, D., Marsman, M., Maris, G., & van der Maas, H. (2018). The Pairwise Exchange Tournament. University of Amsterdam. https://doi.org/10.13140/RG.2.2.26027.54569
  • Open Access
    Marsman, M., Borsboom, D., Kruis, J., Epskamp, S., van Bork, R., Waldorp, L. J., van der Maas, H. L. J., & Maris, G. (2018). An introduction to network psychometrics: Relating ising network models to item response theory models. Multivariate Behavioral Research, 53(1), 15-35. https://doi.org/10.1080/00273171.2017.1379379
  • Open Access
    Ly, A., Marsman, M., & Wagenmakers, E.-J. (2018). Analytic posteriors for Pearson's correlation coefficient. Statistica Neerlandica, 72(1), 4-13. https://doi.org/10.1111/stan.12111
  • Open Access
    Boehm, U., Marsman, M., Matzke, D., & Wagenmakers, E.-J. (2018). On the importance of avoiding shortcuts in applying cognitive models to hierarchical data. Behavior Research Methods, 50(4), 1614-1631. https://doi.org/10.3758/s13428-018-1054-3
  • Open Access
    Wagenmakers, E.-J., Love, J., Marsman, M., Jamil, T., Ly, A., Verhagen, J., Selker, R., Gronau, Q. F., Dropmann, D., Boutin, B., Meerhoff, F., Knight, P., Raj, A., van Kesteren, E.-J., van Doorn, J., Šmíra, M., Epskamp, S., Etz, A., Matzke, D., ... Morey, R. D. (2018). Bayesian inference for psychology. Part II: Example applications with JASP. Psychonomic Bulletin & Review, 25(1), 58-76. https://doi.org/10.3758/s13423-017-1323-7
  • Open Access
    Wagenmakers, E.-J., Marsman, M., Jamil, T., Ly, A., Verhagen, J., Love, J., Selker, R., Gronau, Q. F., Šmíra, M., Epskamp, S., Matzke, D., Rouder, J. N., & Morey, R. D. (2018). Bayesian inference for psychology. Part I: Theoretical advantages and practical ramifications. Psychonomic Bulletin & Review, 25(1), 35-57. https://doi.org/10.3758/s13423-017-1343-3
Page 7 of 10