Dr. Mahdi Shafiee Kamalabad

Dr. Mahdi Shafiee Kamalabad

Assistant Professor
Methodology and Statistics
m.shafieekamalabad@uu.nl


Ahmadi Yazdi, A.; Shafiee Kamalabad, M.; Oberski, D. and Grzegorczyk, M. (2023). Bayesian multivariate control charts for multivariate profiles monitoring. Quality Technology & Quantitative Management,1-36, https://doi.org/10.1080/16843703.2023.2214386.

 

Shafiee Kamalabad, M.; Leenders, R.; & Mulder, J. (2023). What is the Point of Change? Change Point Detection in Relational Event Models. Elsevier, Social Network Vol. 74, (166-181). https://doi.org/10.1016/j.socnet.2023.03.004

 

Fang Q, Giachanou A; Bagheri A; Boeschoten L;  Van Kesteren E;  Shafiee Kamalabad M;  Oberski (2022), On Text-based Personality Computing: Challenges and Future Directions,  arXiv preprint arXiv:2212.06711.

 

Shafiee Kamalabad, M. and Grzegorczyk, M. (2021): A new Bayesian piecewise linear regression model for dynamic network reconstruction. BMC Bioinformatics, 22- (Sup 2), 196.

 

Shafiee Kamalabad, M. and Grzegorczyk, M. (2020): Non-homogeneous dynamic Bayesian networks with edge-wise sequentially coupled parameters. Bioinformatics, 36(4), 1198-1207.

 

Shafiee Kamalabad, M. and Grzegorczyk, M. (2020): A new partially segment-wise coupled piece-wise linear regression model for statistical network structure inference. In Intelligence Methods for Bioinformatics and Biostatistics, Revised Selected Papers, Lecture Notes in Bioinformatics, Springer, 139-152.

 

Shafiee Kamalabad, M.; Heberle, A.M.; Thedieck, K. and Grzegorczyk, M. (2019): Partially non-homogeneous dynamic Bayesian networks based on Bayesian regression models with partitioned design matrices. Bioinformatics, 35 (12), 2108–2117.

 

Shafiee Kamalabad, M., and Grzegorczyk, M. (2018): Improving nonhomogeneous dynamic Bayesian networks with sequentially coupled parameters. Statistica Neerlandica, 72 (3), 281-305.

 

Grzegorczyk, M. and Shafiee Kamalabad, M. (2016): Comparative evaluation of various frequentist and Bayesian non-homogeneous Poisson counting models, Computational Statistics, 32 (1), 1-33.