References

  1. R. Alur, T. Feder & T.A. Henzinger (1996): The benefits of relaxing punctuality. J. ACM. Available at http://doi.acm.org/10.1145/227595.227602.
  2. Y. Annapureddy, C. Liu, G. Fainekos & S. Sankaranarayanan (2011): S-TaLiRo: A Tool for Temporal Logic Falsification for Hybrid Systems. In: Proceedings of TACAS, doi:10.1007/978-3-642-19835-9_21.
  3. C. Baier, E.M. Clarke, V. Hartonas-Garmhausen, M.Z. Kwiatkowska & M. Ryan (1997): Symbolic Model Checking for Probabilistic Processes. In: Proc. of ICALP '97, the 24th International Colloquium on Automata, Languages and Programming, Bologna, Italy, July 7-11, Lecture Notes in Computer Science 1256. Springer Berlin Heidelberg, pp. 430–440, doi:10.1007/3-540-63165-8_199.
  4. C. Baier, B. Haverkort, H. Hermanns & J.-P. Katoen (2003): Model-Checking Algorithms for Continuous-Time Markov Chains. IEEE Trans. Softw. Eng. 29(6), pp. 524–541, doi:10.1109/TSE.2003.1205180.
  5. E. Bartocci, L. Bortolussi & L. Nenzi (2013): A temporal logic approach to modular design of synthetic biological circuits. In: In Proc. of CMSB 2013, the 11th International Conference on Computational Methods in Systems Biology, IST Austria, Klosterneuburg, Austria, September 23-25, 2013, Lecture Notes in Computer Science 8130, Springer-Verlag, pp. 164–178, doi:10.1007/978-3-642-39176-7.
  6. E. Bartocci, R. Grosu, P. Katsaros, C. Ramakrishnan & S. A. Smolka (2011): Model Repair for Probabilistic Systems. In: Proceedings of TACAS 2011, the 17th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, Lecture Notes in Computer Science 6605. Springer Berlin / Heidelberg, pp. 326–340, doi:10.1007/978-3-642-19835-9_30.
  7. C. M. Bishop (2006): Pattern Recognition and Machine Learning. Springer.
  8. L. Bortolussi (2010): Limit behavior of the hybrid approximation of Stochastic Process Algebras. In: Proceedings of ASMTA 2010, doi:10.1007/978-3-642-13568-2_26.
  9. L. Bortolussi, J. Hillston, D. Latella & M. Massink (2013): Continuous Approximation of Collective Systems Behaviour: a Tutorial. Performance Evaluation 70(5), pp. 317–349, doi:10.1016/j.peva.2013.01.001.
  10. L. Bortolussi & A. Policriti (2008): Hybrid approximation of stochastic process algebras for systems biology. In: Proceedings of IFAC WC, doi:10.3182/20080706-5-KR-1001.02132.
  11. L. Bortolussi & A. Policriti (2010): Hybrid Dynamics of Stochastic Programs. Theoretical Computer Science 411(20), pp. 2052–2077, doi:10.1016/j.tcs.2010.02.008.
  12. L. Bortolussi & A. Policriti (in print): (Hybrid) Automata and (Stochastic) Programs. The hybrid automata lattice of a stochastic program. Journal of Logic and Computation.
  13. L. Bortolussi & G. Sanguinetti (2013): Learning and Designing Stochastic Processes from Logical Constraints. In: Proc. of QEST 2013, 10th International Conference on Quantitative Evaluation of Systems, Buenos Aires, Argentina, August 27-30, 2013 8054, pp. 89–105, doi:10.1007/978-3-642-40196-1.
  14. M. L. Bujorianu, J. Lygeros & M. C. Bujorianu (2005): Bisimulation for General Stochastic Hybrid Systems. In: Proceedings of HSCCl, pp. 198–214, doi:10.1007/978-3-540-31954-2_13.
  15. L. Calzone, F. Fages & S. Soliman (2006): BIOCHAM: an environment for modeling biological systems and formalizing experimental knowledge. Bioinformatics 22, pp. 1805–1807, doi:10.1093/bioinformatics/btl172.
  16. T. Chen, M. Diciolla, M. Kwiatkowska & A. Mereacre (2011): Time-bounded verification of CTMCs against real-time specifications. In: Proc. of FORMATS 2011, the 9th International Conference on Formal Modeling and Analysis of Timed Systems, Aalborg, Denmark, September 21–23, Lecture Notes in Computer Science 6919, Berlin, Heidelberg, pp. 26–42, doi:10.1007/978-3-642-24310-3_4.
  17. M.H.A. Davis (1993): Markov Models and Optimization. Chapman & Hall.
  18. Andrea Degasperi & Stephen Gilmore (2008): Sensitivity analysis of stochastic models of bistable biochemical reactions. In: Formal Methods for Computational Systems Biology, Lecture Notes in Computer Science 5016. Springer, pp. 1–20, doi:10.1007/978-3-540-68894-5_1.
  19. A. Donzé (2010): Breach, A Toolbox for Verification and Parameter Synthesis of Hybrid Systems. In: Proceedings of CAV. Available at http://dx.doi.org/10.1007/978-3-642-14295-6_17.
  20. A. Donzé, G. Clermont & C.J. Langmead (2010): Parameter Synthesis in Nonlinear Dynamical Systems: Application to Systems Biology. Journal of Computational Biology 17(3), pp. 325–336, doi:10.1007/978-3-642-02008-7_11.
  21. A. Donzé, E. Fanchon, L. M. Gattepaille, O. Maler & P. Tracqui (2011): Robustness analysis and behavior discrimination in enzymatic reaction networks. PLoS One 6(9), pp. e24246, doi:10.1371/journal.pone.0024246.
  22. A. Donzé, T. Ferrer & O. Maler (2013): Efficient Robust Monitoring for STL. In: Proc. of CAV 2013, the 25th International Conference on Computer Aided Verification, Saint Petersburg, Russia, July 13-19, Lecture Notes in Computer Science 8044, pp. 264–279, doi:10.1007/978-3-642-39799-8_19.
  23. A. Donzé & O. Maler (2010): Robust satisfaction of temporal logic over real-valued signals. In: Proc. of FORMATS 2010, the 8th International Conference on Formal Modeling and Analysis of Timed Systems, Klosterneuburg, Austria, September 8–10 6246, pp. 92–106, doi:10.1007/978-3-642-15297-9_9.
  24. R. Durett (2012): Essentials of stochastic processes. Springer, doi:10.1007/978-1-4614-3615-7.
  25. M.B. Elowitz & S. Leibler (2000): A synthetic oscillatory network of transcriptional regulators. Nature 403, pp. 335–338, doi:10.1038/35002125.
  26. G. Fainekos & G. Pappas (2007): Robust Sampling for MITL Specifications. In: Proc. of FORMATS 2007, the 5th International Conference on Formal Modeling and Analysis of Timed Systems, Lecture Notes in Computer Science 8044, pp. 264–279, doi:10.1007/978-3-540-75454-1_12.
  27. G. E. Fainekos & G. J. Pappas (2009): Robustness of temporal logic specifications for continuous-time signals. Theor. Comput. Sci. 410(42), pp. 4262–4291, doi:10.1016/j.tcs.2009.06.021.
  28. A. Georgoulas, A. Clark, A. Ocone, S. Gilmore & G. Sanguinetti (2012): A subsystems approach for parameter estimation of ODE models of hybrid systems. In: Proc. of HSB 2012, the 1st International Workshop on Hybrid Systems and Biology, EPTCS 92, pp. 30–41, doi:10.4204/EPTCS.92.3.
  29. D.T. Gillespie (1977): Exact Stochastic Simulation of Coupled Chemical Reactions. J. of Physical Chemistry 81(25), doi:10.1021/j100540a008.
  30. R. Gunawan, Y. Cao, L. Petzold & F.J. Doyle III (2005): Sensitivity analysis of discrete stochastic systems. Biophysical Journal 88(4), pp. 2530, doi:10.1529/biophysj.104.053405.
  31. K. D. Jones, Konrad V & D. Nickovic (2010): Analog property checkers: a DDR2 case study. Formal Methods in System Design 36(2), pp. 114–130, doi:10.1007/s10703-009-0085-x.
  32. M. Kennedy & A. O'Hagan (2001): Bayesian Calibration of Computer Models. Journal of the Royal Stat. Soc. Ser. B 63(3), pp. 425–464, doi:10.1111/1467-9868.00294.
  33. M. Komorowski, M. J. Costa, D. A. Rand & M. PH Stumpf (2011): Sensitivity, robustness, and identifiability in stochastic chemical kinetics models. PNAS USA 108(21), pp. 8645–8650, doi:10.1073/pnas.1015814108.
  34. Marta Kwiatkowska, Gethin Norman & David Parker (2004): Probabilistic symbolic model checking with PRISM: a hybrid approach. Int. J. Softw. Tools Technol. Transf. 6(2), pp. 128–142, doi:10.1007/s10009-004-0140-2.
  35. S. Drazan L. Brim, M. Ceska & D. Šafránek (2013): Exploring Parameter Space of Stochastic Biochemical Systems using Quantitative Model Checking. In: Proc. of CAV 2013, the 25th International Conference on Computer Aided Verification, Saint Petersburg, Russia, July 13-19, Lecture Notes in Computer Science 8044, pp. 107–123, doi:10.1007/978-3-642-39799-8_7.
  36. O. Maler & D. Nickovic (2004): Monitoring Temporal Properties of Continuous Signals. In: Proc. of Joint International Conferences on Formal Modeling and Analysis of Timed Systmes, FORMATS 2004, and Formal Techniques in Real-Time and Fault -Tolerant Systems, FTRTFT 2004, Grenoble, France, September 22-24 3253, pp. 152–166, doi:10.1007/978-3-540-30206-3_12.
  37. A. Ocone, A. J. Millar & G. Sanguinetti (2013): Hybrid regulatory models: a statistically tractable approach to model regulatory network dynamics. Bioinformatics 29(7), pp. 910–916, doi:10.1093/bioinformatics/btt06.
  38. M. Opper, A. Ruttor & G. Sanguinetti (2010): Approximate inference in continuous time Gaussian-Jump processes. In: Proceedings of NIPS 2010, the 4th Annual Conference on Neural Information Processing Systems, 6–9 December 2010, Vancouver, British Columbia, Canada, pp. 1831–1839. Available at http://books.nips.cc/papers/files/nips23/NIPS2010_1095.pdf.
  39. Amir Pnueli (1977): The temporal logic of programs. Foundations of Computer Science, IEEE Annual Symposium on 0, pp. 46–57, doi:10.1109/SFCS.1977.32.
  40. C. E. Rasmussen & C. K. I. Williams (2006): Gaussian Processes for Machine Learning. MIT Press.
  41. A. Rizk, G. Batt, F. Fages & S. Soliman (2008): On a Continuous Degree of Satisfaction of Temporal Logic Formulae with Applications to Systems Biology. In: Proc. of CMSB 2008, the 6th International Conference on Computational Methods in Systems Biology, Rostock, Germany, October 12–15, Lecture Notes in Computer Science 5307, pp. 251–268, doi:10.1007/978-3-540-88562-7_19.
  42. Niranjan Srinivas, Andreas Krause, Sham M. Kakade & Matthias W. Seeger (2012): Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting. IEEE Transactions on Information Theory 58(5), pp. 3250–3265, doi:10.1109/TIT.2011.2182033.
  43. H. L. S. Younes, M. Z. Kwiatkowska, G. Norman & D. Parker (2004): Numerical vs. Statistical Probabilistic Model Checking: An Empirical Study. In: Proc. of 2004, the 10th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, Barcelona, Spain, March 29 - April 2, doi:10.1007/978-3-540-24730-2_4.

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