Google Scholar Page

Submitted for publication

Sekulovski, N., Marsman, M., & Wagenmakers, E.-J. (2024). A Good Check on the Bayes Factor. PsyArXiv.

Sekulovski, N., Keetelaar, S., Haslbeck, J. M. B., & Marsman, M. (2023). Sensitivity Analysis of Prior Distributions in Bayesian Graphical Modeling: Guiding Informed Prior Choices for Conditional Independence Testing. PsyArXiv.

Huth, K. B. S., Zavlis, O., Luigjes, J., Galenkamp, H., Lok, A., Bockting, C., Goudriaan, A. E., Marsman, M., & van Holst, R. J. (2024). A Network Perspective on Ethnic, Religious, and Socioeconomic Factors in Alcohol Use—the HELIUS study. PsyArXiv

Marsman, M., van den Bergh, D., & Haslbeck, J. M. B. (2023). Bayesian Analysis of the Ordinal Markov Random Field. PsyArXiv

van den Bergh, D., & Dablander, F. (2022). Flexible Bayesian Multiple Comparison Adjustment Using Dirichlet Process and Beta-Binomial Model Priors. arXiv.

Accepted for publication

In press

Sekulovski, N., Keetelaar, S., Huth, K. B. S., Wagenmakers, E.-J., van Bork, R., van den Bergh, D., & Marsman, M. (in press). Testing Conditional Independence in Psychometric Networks: An Analysis of Three Bayesian Methods. Multivariate Behavioral Research.

Hoogeveen, S., Borsboom, D., Kucharský, Š, Marsman, M., Molenaar, D., de Ron, J., Sekulovski, N., Visser, I., van Elk, M., & Wagenmakers, E.-J. (in press). Prevalence, Patterns, and Predictors of Paranormal Beliefs in the Netherlands: A Several-Analysts Approach. Royal Society Open Science.


Keetelaar, S., Sekulovski, N., Borsboom, D., & Marsman, M. (2024). Comparing Maximum Likelihood and Pseudo-Maximum Likelihood Estimators for the Ising Model. Advances .in/psychology.

Huth, K., Keetelaar, S., Sekulovski, N., van den Bergh, D., & Marsman, M. (2024). Simplifying Bayesian analysis of graphical models for the social sciences with easybgm: A user-friendly R-package. Advances .in/psychology.


Huth, K. B. S., de Ron, J., Goudriaan, A. E., Luigjes, J., Mohammadi, R., van Holst, R. J., Wagenmakers, E.-J., & Marsman, M. (2023). Bayesian analysis of cross-sectional networks: A tutorial in R and JASP. Advances in Methods and Practices in Psychological Science, 6, 1-18.

Marsman, M., & Huth, K. B. S. (2023). Idiographic Ising and Divide and Color Models: Encompassing networks for heterogeneous binary data. Multivariate Behavioral Research, 58, 787-814.

Marsman, M., Waldorp, L. J., & Borsboom, D. (2023). Towards an encompassing theory of network models: Reply to Brusco, Steinley, Hoffman, Davis-Stober, & Wasserman. Psychological Methods, 28, 757-764.


Huth, K. B. S., Waldorp, L. J., Luigjes, J., Goudriaan, A. E., van Holst, R. J., & Marsman, M. (2022). A note on the Structural Change Test in finite samples: Using a permutation approach to estimate the sampling distribution. Psychometrika, 87, 1064-2080.

Marsman, M., & Rhemtulla, M. (2022). Guest Editors’ Introduction to The Special Issue “Network Psychometrics in Action”: Methodological Innovations Inspired by Empirical Problems. Psychometrika, 87, 1–11.

Marsman, M., Huth, K., Waldorp, L. J., & Ntzoufras, I. (2022). Objective Bayesian Edge Screening and Structure Selection for Networks of Binary Variables. Psychometrika, 87, 47–82.

Waldorp, L. J., & Marsman, M. (2022). Relations between networks, regression, partial correlation, and latent variable models. Multivariate Behavioral Research, 57, 994-1006.


Haslbeck, J., Epskamp, S., Marsman, M., & Waldorp, L. J. (2021). Interpreting the Ising model: The input matters. Multivariate Behavioral Research, 56, 303-313.

Huth, K. B. S., Luigjes, J., Marsman, M., Goudriaan, A. E., & van Holst, R. J. (2021). Modeling alcohol use disorder as a set of interconnected symptoms -Assessing differences between clinical and populationsamples and across external factors. Addictive Behaviors, 125, 107128.


Marsman, M., Sigurdardottir, H., Bolsinova, M., & Maris, G. K. J. (2019). Characterizing the manifest probability distributions of three latent trait models for accuracy and response time. Psychometrika, 84, 870-891.

Marsman, M., Tanis, C., Bechger, T. M., & Waldorp, L. J. (2019). Network psychometrics in educational practice. Maximum likelihood estimation of the Curie-Weiss model. In Theoretical and Practical Advances in Computer-Based Educational Measurement (pp. 93–120). Springer Nature.

Waldorp, L. J., Marsman, M., & Maris, G. K. J. (2019). Logistic regression and Ising networks: Prediction and estimation when violating lasso assumptions. Behaviormetrika, 46, 49-72.


Marsman, M., Borsboom, D., Kruis, J., Epskamp, S., van Bork, R., Waldorp, L. J., Maas, H. L. J. van der, & Maris, G. (2018). An introduction to network psychometrics: Relating Ising network models to item response theory models. Multivariate Behavioral Research, 53, 15–35.


Epskamp, S., Kruis, J., & Marsman, M. (2017). Estimating psychopathological networks: Be careful what you wish for. PloS One, 12, e0179891.

Marsman, M., Waldorp, L. J. & Maris, G. K. J. (2017). A note on large-scale logistic prediction: Using an approximate graphical model to deal with collinearity and missing data. Behaviormetrika, 44, 513-534.


Marsman, M., Maris, G., Bechger, T., & Glas, C. (2015). Bayesian inference for low-rank Ising networks. Scientific Reports, 5, 9050.