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Results: 346
Number of items: 346
  • Delsing, G. A., Mandjes, M. R. H., Spreij, P. J. C., & Winands, E. M. M. (2019). An optimization approach to adaptive multi-dimensional capital management. Insurance: Mathematics and Economics, 84, 87-97. https://doi.org/10.1016/j.insmatheco.2018.10.001
  • Frolkova, M., & Mandjes, M. (2019). A Bitcoin-inspired infinite-server model with a random fluid limit. Stochastic Models, 35(1), 1-32. https://doi.org/10.1080/15326349.2018.1559739
  • Mandjes, M., & Robert, P. (2019). Introduction to special issue: The IFIP Performance 2018 conference. Queueing Systems, 91(3-4), 205-206. https://doi.org/10.1007/s11134-019-09607-0
  • Bisewski, K., Crommelin, D., & Mandjes, M. (2019). Rare event simulation for steady-state probabilities via recurrency cycles. Chaos, 29(3), Article 033131. https://doi.org/10.1063/1.5080296
  • Jansen, H. M., Mandjes, M., De Turck, K., & Wittevrongel, S. (2019). Diffusion limits for networks of Markov-modulated infinite-server queues. Performance Evaluation, 135, 18. Article 102039. https://doi.org/10.1016/j.peva.2019.102039
  • Open Access
    de Gunst, M., Knapik, B., Mandjes, M., & Sollie, B. (2019). Parameter estimation for a discretely observed population process under Markov-modulation. Computational Statistics and Data Analysis, 140, 88-103. https://doi.org/10.1016/j.csda.2019.06.008
  • Open Access
    Boxma, O., Kella, O., & Mandjes, M. (2019). Infinite-server systems with Coxian arrivals. Queueing Systems, 92(3-4), 233-255. https://doi.org/10.1007/s11134-019-09613-2
  • Open Access
    Mandjes, M., Starreveld, N., Bekker, R., & Spreij, P. (2019). Dynamic Erdős-Rényi Graphs. In B. Steffen, & G. Woeginger (Eds.), Computing and Software Science : State of the Art and Perspectives (pp. 123-140). (Lecture Notes in Computer Science; Vol. 10000). Springer. https://doi.org/10.48550/arXiv.1703.05505, https://doi.org/10.1007/978-3-319-91908-9_8
  • Open Access
    Boxma, O. J., Cahen, E. J., Koops, D., & Mandjes, M. (2019). Linear Stochastic Fluid Networks: Rare-Event Simulation and Markov Modulation. Methodology and Computing in Applied Probability, 21(1), 125–153. https://doi.org/10.1007/s11009-018-9644-1
  • Open Access
    Zhou, H., Dorsman, J. L., Snelder, M., Romph, de, E., & Mandjes, M. R. H. (2019). GPU-based Parallel Computing for Activity-based Travel Demand Models. In E. Shakshuki (Ed.), The 10th International Conference on Ambient Systems, Networks and Technologies (ANT 2019) / The 2nd International Conference on Emerging Data and Industry 4.0 (EDI40 2019) / Affiliated Workshops (Vol. 151, pp. 726-732). (Procedia Computer Science). Elsevier. https://doi.org/10.1016/j.procs.2019.04.097
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