Download PDF by Bruno Sericola, Telek Miklós, Gábor Horváth: Analytical and Stochastic Modeling Techniques and

By Bruno Sericola, Telek Miklós, Gábor Horváth

ISBN-10: 3319082183

ISBN-13: 9783319082189

ISBN-10: 3319082191

ISBN-13: 9783319082196

This e-book constitutes the refereed complaints of the twenty first overseas convention on Analytical and Stochastic Modelling ideas and purposes, ASMTA 2014, held in Budapest, Hungary, in June/July 2014. The 18 papers provided have been conscientiously reviewed and chosen from 27 submissions. The papers talk about the most recent advancements in analytical, numerical and simulation algorithms for stochastic structures, together with Markov techniques, queueing networks, stochastic Petri nets, strategy algebras, video game idea, etc.

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Read Online or Download Analytical and Stochastic Modeling Techniques and Applications: 21st International Conference, ASMTA 2014, Budapest, Hungary, June 30 – July 2, 2014. Proceedings PDF

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Additional resources for Analytical and Stochastic Modeling Techniques and Applications: 21st International Conference, ASMTA 2014, Budapest, Hungary, June 30 – July 2, 2014. Proceedings

Example text

Determining the condition for which a function v(l, x) does not lead to negative counts, it is an important topic that will be investigated in future woks: here we will limit ourselves to consider functions coming from system biology, that are known to correctly behave as reaction rates. In particular we refer to the Law of Mass Action (Waage and Guldberg, 1864), which tells that the reaction rate is proportional to the probability of a collision of the reactants, that in turn is proportional to the concentration of reactants (agents in our case), elevated to the multiplicity required to start the reaction: vh (l, x) = γh xj (l ) j:∃l ∈L,n− j,h (l,l )>0 l ∈L n− j,h (l, l ) (6) l ∈L Here γv represents the speed at which the reaction occurs.

The same technique can also be used to model other spatial based system, such as wireless networks [2], economical evolution of firms [16] and many more. References 1. : Analysis of large scale interacting systems by mean field method. In: 5th International Conference on Quantitative Evaluation of Systems - QEST 2008, St. Malo (2008) 2. : Studying mobile internet technologies with agent based mean-field models. , De Turck, K. ) ASMTA 2013. LNCS, vol. 7984, pp. 112–126. Springer, Heidelberg (2013) 3.

Investigating the link between epithelial-mesenchymal transition and the cancer stem cell phenotype: A mathematical approach. Journal of Theoretical Biology 265, 329–335 (2010) 7. : Markovian agent modeling swarm intelligence algorithms in wireless sensor networks. Perform. Eval. 69(3-4), 135–149 (2012) 8. : Computational Analysis of Biochemical Systems. Cambridge University Press (2000) 9. : Modeling biological pathways: an objectoriented like methodology based on mean field analysis. In: The Third International Conference on Advanced Engineering Computing and Applications in Sciences (ADVCOM), pp.

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Analytical and Stochastic Modeling Techniques and Applications: 21st International Conference, ASMTA 2014, Budapest, Hungary, June 30 – July 2, 2014. Proceedings by Bruno Sericola, Telek Miklós, Gábor Horváth


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