PuSH - Publikationsserver des Helmholtz Zentrums München

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1.
Stapor, P. et al.: PESTO: Parameter EStimation TOolbox. Bioinformatics 34, 705-707 (2018)
2.
Ballnus, B. et al.: Comprehensive benchmarking of Markov chain Monte Carlo methods for dynamical systems. BMC Syst. Biol. 11:63 (2017)
3.
Hug, S. ; Schmidl, D. ; Li, W.B. ; Greiter, M. & Theis, F.J.: Bayesian model selection methods and their application to biological ODE systems. In: Geris, L.* ; Gomez-Cabrero, D.* [Eds.]: Uncertainty in Biology : A Computational Modeling Approach. Berlin; Heidelberg: Springer, 2016. 243-268 (Stud. Mechanobiol. Tiss. Engineering Biomater. ; 17)
4.
Hug, S.: From low-dimensional model selection to high-dimensional inference: tailoring Bayesian methods to biological dynamical systems. München, Technische Universität, Fakultät für Mathematik, Diss., 2015, 212 S.
5.
Hug, S. ; Schwarzfischer, M. ; Hasenauer, J. ; Marr, C. & Theis, F.J.: An adaptive scheduling scheme for calculating Bayes factors with thermodynamic integration using Simpson’s rule. Stat. Comp. 26, 663-677 (2015)
6.
Hug, S. et al.: High-dimensional Bayesian parameter estimation: Case study for a model of JAK2/STAT5 signaling. Math. Biosci. 246, 293-304 (2013)
7.
Raue, A. et al.: Lessons learned from quantitative dynamical modeling in systems biology. PLoS ONE 8:e74335 (2013)
8.
Schmidl, D. ; Czado, C.* ; Hug, S. & Theis, F.J.: A vine-copula based adaptive MCMC sampler for efficient inference of dynamical systems. Bayesian Anal. 8, 1-22 (2013)
9.
Schmidl, D. ; Czado, C.* ; Hug, S. & Theis, F.J.: Rejoinder on "A vine-copula based adaptive MCMC sampler for efficient inference of dynamical systems". Bayesian Anal. 8, 33-42 (2013)
10.
Vehlow, C.* et al.: iVUN: Interactive Visualization of Uncertain biochemical reaction Networks. BMC Bioinformatics 14:S2 (2013)
11.
Hug, S. & Theis, F.J.: Bayesian inference of latent causes in gene regulatory dynamics. In: Theis, F.J. ; Cichocki, A.* ; Yeredor, A* ; Zibulevsky, M.* [Eds.]: Proceedings (10th international conference on Latent Variable Analysis and Signal Separation). Heidelberg: Springer, 2012. 520-527 (Lecture Notes Comp. Sci. ; 7191)
12.
Schmidl, D. ; Hug, S. ; Li, W.B. ; Greiter, M. & Theis, F.J.: Bayesian model selection validates a biokinetic model for zirconium processing in humans. BMC Syst. Biol. 6:95 (2012)