1. PRISM Home Page.
  2. M. Arns, P. Buchholz & A. Panchenko (2010): On the Numerical Analysis of Inhomogeneous Continuous-Time Markov Chains. INFORMS Journal on Computing 22(3), pp. 416–432, doi:10.1287/ijoc.1090.0357.
  3. A. Aziz, V. Singhal, F. Balarin, R. Brayton & A. Sangiovanni-Vincentelli (1996): Verifying Continuous Time Markov Chains. In: Proceedings of CAV96, doi:10.1007/3-540-61474-5_75.
  4. C. Baier, B.R. Haverkort, H. Hermanns & J.P. Katoen (2003): Model-Checking Algorithms for Continuous-Time Markov Chains. IEEE Trans. Software Eng. 29(6), pp. 524–541, doi:10.1109/TSE.2003.1205180.
  5. M. Benaïm & J. Le Boudec (2008): A Class of Mean Field Interaction Models for Computer and Communication Systems. Performance Evaluation 65(11-12), pp. 823–838, doi:10.1016/j.peva.2008.03.005.
  6. M. Benaïm & J.Y. Le Boudec (2011): On Mean Field Convergence and Stationary Regime. CoRR abs/1111.5710. Available at
  7. P. Billingsley (1979): Probability and Measure. John Wiley and Sons.
  8. P. Billingsley (1999): Convergence of Probability Measures, 2nd Edition. Wiley, doi:10.1002/9780470316962.
  9. L. Bortolussi (2008): On the Approximation of Stochastic Concurrent Constraint Programming by Master Equation. In: Proceedings of QAPL, pp. 163–180, doi:10.1016/j.entcs.2008.11.025.
  10. L. Bortolussi, V. Galpin & J. Hillston (2012): Hybrid performance modelling of opportunistic networks. In: Proceedings of QAPL 2012, EPTCS 85, pp. 106121, doi:10.4204/EPTCS.85.8.
  11. L. Bortolussi & J. Hillston (2012): Fluid Model Checking. In: Proceedings of CONCUR 2012, doi:10.1007/978-3-642-32940-1_24.
  12. L. Bortolussi & J. Hillston (2015): Model Checking Single Agent Behaviour by Fluid Approximation. Information and Computation 242, pp. 183–226, doi:10.1016/j.ic.2015.03.002.
  13. L. Bortolussi, J. Hillston, D. Latella & M. Massink (2013): Continuous Approximation of Collective Systems Behaviour: a Tutorial. Perf. Eval. 70(5), pp. 317–349, doi:10.1016/j.peva.2013.01.001.
  14. L. Bortolussi & R. Lanciani (2013): Model Checking Markov Population Models by Central Limit Approximation. In: Proceedings of QEST, pp. 123–138, doi:10.1007/978-3-642-40196-1_9.
  15. E. Clarke, A. Peled & A. Grunberg (1999): Model Checking. MIT press.
  16. R.W.R. Darling (2002): Fluid Limits of Pure Jump Markov Processes: A Practical Guide.
  17. R.W.R. Darling & J.R. Norris (2008): Differential equation approximations for Markov chains. Probability Surveys 5, doi:10.1214/07-PS121.
  18. N. Gast & B. Gaujal (2010): A mean field model of work stealing in large-scale systems. In: Proceedings of ACM SIGMETRICS 2010, pp. 13–24. Available at
  19. R. A. Hayden, J. T. Bradley & A. Clark (2013): Performance Specification and Evaluation with Unified Stochastic Probes and Fluid Analysis. IEEE Trans. Software Eng. 39(1), pp. 97–118, doi:10.1109/TSE.2012.1.
  20. R.A. Howard (2007): Dynamic Probabilistic Systems, Volume II. Dover.
  21. A. Kolesnichenko, A. Remke, P.T. de Boer & B.R. Haverkort (2011): Comparison of the Mean-Field Approach and Simulation in a Peer-to-Peer Botnet Case Study. In: Proceedings of EPEW, pp. 133–147, doi:10.1007/978-3-642-24749-1_11.
  22. S. Krantz & P.R. Harold (2002): A Primer of Real Analytic Functions (Second ed.). Birkhäuser, doi:10.1007/978-0-8176-8134-0.
  23. T. G. Kurtz (1970): Solutions of Ordinary Differential Equations as Limits of Pure Jump Markov Processes. Journal of Applied Probability 7, pp. 49–58, doi:10.2307/3212147.
  24. D. Latella, M. Loreti & M. Massink (2013): On-the-fly Fast Mean-Field Model-Checking. In: Proceedings of TGC, pp. 297–314, doi:10.1007/978-3-319-05119-2_17.
  25. J.-Y. Le Boudec (2010): Performance Evaluation of Computer and Communication Systems. EPFL Press, Lausanne..
  26. M. Massink, D. Latella, A. Bracciali, M. Harrison & J. Hillston (2012): Scalable context-dependent analysis of emergency egress models. Formal Aspects of Computing, pp. 1–36, doi:10.1007/s00165-011-0188-1.
  27. J. R. Norris (1997): Markov Chains. Cambridge University Press, doi:10.1017/CBO9780511810633.
  28. H. Qian & E.L. Elson (2002): Single-molecule enzymology: stochastic Michaelis-Menten kinetics. Biophysical Chemistry 101, pp. 565–576, doi:10.1016/S0301-4622(02)00145-X.
  29. J. Rutten, M. Kwiatkowska, G. Norman & D. Parker (2004): Mathematical Techniques for Analyzing Concurrent and Probabilistic Systems. CRM Monograph Series 23. American Mathematical Society.
  30. A. Stefanek, R. A. Hayden, M. Mac Gonagle & J. T. Bradley (2012): Mean-Field Analysis of Markov Models with Reward Feedback. In: Proceedings of ASMTA 2012, pp. 193–211, doi:10.1007/978-3-642-30782-9_14.
  31. D.T.J. Sumpter (2000): From Bee to Society: An Agent-based Investigation of Honey Bee Colonies. University of Manchester.
  32. Z. Szallasi, J. Stelling & V. Periwal (2012): System Modeling in Cellular Biology, From Concepts to Nuts and Bolts. MIT Press.
  33. M. Tribastone, J. Ding, S. Gilmore & J. Hillston (2012): Fluid Rewards for a Stochastic Process Algebra. IEEE Trans. Software Eng. 38(4), pp. 861–874, doi:10.1109/TSE.2011.81.
  34. N. G. Van Kampen (1992): Stochastic Processes in Physics and Chemistry. Elsevier.

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