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Peter J. Haas, "Estimation Methods for Nonregenerative Stochastic Petri Nets," IEEE Transactions on Software Engineering, vol. 25, no. 2, pp. 218236, March/April, 1999.  
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@article{ 10.1109/32.761447, author = {Peter J. Haas}, title = {Estimation Methods for Nonregenerative Stochastic Petri Nets}, journal ={IEEE Transactions on Software Engineering}, volume = {25}, number = {2}, issn = {00985589}, year = {1999}, pages = {218236}, doi = {http://doi.ieeecomputersociety.org/10.1109/32.761447}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE Transactions on Software Engineering TI  Estimation Methods for Nonregenerative Stochastic Petri Nets IS  2 SN  00985589 SP218 EP236 EPD  218236 A1  Peter J. Haas, PY  1999 KW  Stochastic Petri nets KW  stochastic simulation KW  discreteevent stochastic systems KW  standardized time series KW  batch means KW  modeling power KW  video on demand KW  Harris recurrence KW  Markov chains KW  generalized semiMarkov processes KW  stability. VL  25 JA  IEEE Transactions on Software Engineering ER   
Abstract—When a computer, manufacturing, telecommunication, or transportation system is modeled as a stochastic Petri net (SPN), many longrun performance characteristics of interest can be expressed as timeaverage limits of the associated marking process. For nets with generallydistributed firing times, such limits often cannot be computed analytically or numerically, but must be estimated using simulation. Previous work on estimation methods for SPNs has focused on the case in which there exists a sequence of regeneration points for the marking process of the net, so that point estimates and confidence intervals for timeaverage limits can be obtained using the regenerative method for analysis of simulation output. This paper is concerned with SPNs for which the regenerative method is not applicable. We provide conditions on the clocksetting distributions and newmarking probabilities of an SPN under which timeaverage limits are well defined and the output process of the simulation obeys a multivariate functional central limit theorem. It then follows from results of Glynn and Iglehart [9] that methods based on standardized time series can be used to obtain strongly consistent point estimates and asymptotic confidence intervals for timeaverage limits. In particular, the method of batch means is applicable. Moreover, the methods of Muñoz and Glynn can be used to obtain point estimates and confidence intervals for ratios of timeaverage limits. We illustrate our results using an SPN model of an interactive videoondemand system.
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