By Sabine Wittevrongel, Tuan Phung-Duc
This publication constitutes the refereed court cases of the twenty third foreign convention on Analytical and Stochastic Modelling options and functions, ASMTA 2016, held in Cardiff, united kingdom, in August 2016.
The 21 complete papers provided during this e-book have been rigorously reviewed and chosen from 30 submissions. The papers talk about the newest advancements in analytical, numerical and simulation algorithms for stochastic structures, together with Markov methods, queueing networks, stochastic Petri nets, procedure algebras, video game conception, etc.
Read Online or Download Analytical and Stochastic Modelling Techniques and Applications: 23rd International Conference, ASMTA 2016, Cardiff, UK, August 24-26, 2016, Proceedings PDF
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Additional resources for Analytical and Stochastic Modelling Techniques and Applications: 23rd International Conference, ASMTA 2016, Cardiff, UK, August 24-26, 2016, Proceedings
The quality of an estimator does not depend only on its variance, but also on its bias: μSA ] − μ = < g, π > −μ. Bias(ˆ μSA ) = E[ˆ (7) Then the mean squared error of the estimator, M SE(ˆ μSA ), is: μSA )2 + Var(ˆ μSA ). M SE(ˆ μSA ) = Bias(ˆ (8) This bias can be non-null if the quantity we want to estimate is correlated with the degree. In fact, we observe that the random walk visits the nodes with more connections more frequently. Subsampling has no eﬀect on such bias, hence minimizing the variance leads to minimizing the mean squared error.
Synthetic Datasets. To perform extensive simulations we needed more graph structures with node attributes. There is no lack of available real network topologies. For example, the Stanford Large Network Dataset Collection  provides data of Online-Social Networks (we will use part of Facebook graph), collaboration networks, web graphs, Internet peer-to-peer network and a lot of others. Unfortunately, in most of the cases, nodes do not have any attribute. g. number of nodes, links, degree distribution, clustering coefﬁcient).
J. Oﬀ. Stat. be Department of Industrial Systems Engineering and Product Design, Ghent University, Zwijnaarde, Belgium Abstract. Due to their wide area of applications, queueing models with batch service, where the server can process several customers simultaneously, have been studied frequently. An important characteristic of such batch-service systems is the size of a batch, that is the number of customers that are processed simultaneously. In this paper, we analyse a two-class batch-service queueing model with variable server capacity, where all customers are accommodated in a common ﬁrst-come-ﬁrst served single-server queue.