... Non-Markovian Stochastic Petri Nets Andrea Bobbio Dipartimento di Informatica ... Johns Hopki... more ... Non-Markovian Stochastic Petri Nets Andrea Bobbio Dipartimento di Informatica ... Johns Hopkins University Press, Baltimore, 1981. 17] M. Telek, A. Bobbio, L. Jereb, A. Pulia to, and K. Trivedi. Steady state analysis of Markov regenerative SPN with age memory policy. ...
Page 1. New Primitives for Interlaced Memory Policies in Markov Regenerative Stochastic Petri Net... more Page 1. New Primitives for Interlaced Memory Policies in Markov Regenerative Stochastic Petri Nets Andrea Bobbiol Antonio Puliafito2 Mikl6s Telek3 Dipartimento di Informatica, Universita di Torino 10149 Torino, Italy; e-mai1:bobbioQdi.unito.it ...
... 3. Numerical techniques in dependability analysis While fault trees are solved by resorting t... more ... 3. Numerical techniques in dependability analysis While fault trees are solved by resorting to combin-atoriai techniques and Boolean algebra, the modell-ing techniques surveyed in 2.1, 2.2 and 2.3 lead to the formulation of stochastic point processes. ...
Page 1. Non-Exponential Stochastic Petri Nets: an Overview of Methods and Techniques PART 1: Sema... more Page 1. Non-Exponential Stochastic Petri Nets: an Overview of Methods and Techniques PART 1: Semantics and Speci cations Andrea Bobbio Dipartimento di Informatica Universit a di Torino, 10149 Torino, Italy PART 2: Solution Techniques ...
... MISC{Bobbio93thetask, author = {Andrea Bobbio and Miklós Telek}, title = {The Task Completion... more ... MISC{Bobbio93thetask, author = {Andrea Bobbio and Miklós Telek}, title = {The Task Completion Time in Degradable Systems}, year = {1993} }. ... 1987. 12, The Completion Time of Programs on Processors Subject to Failure and Repair Chimento, Trivedi - 1993. ...
... Non-Markovian Stochastic Petri Nets Andrea Bobbio Dipartimento di Informatica ... Johns Hopki... more ... Non-Markovian Stochastic Petri Nets Andrea Bobbio Dipartimento di Informatica ... Johns Hopkins University Press, Baltimore, 1981. 17] M. Telek, A. Bobbio, L. Jereb, A. Pulia to, and K. Trivedi. Steady state analysis of Markov regenerative SPN with age memory policy. ...
Page 1. New Primitives for Interlaced Memory Policies in Markov Regenerative Stochastic Petri Net... more Page 1. New Primitives for Interlaced Memory Policies in Markov Regenerative Stochastic Petri Nets Andrea Bobbiol Antonio Puliafito2 Mikl6s Telek3 Dipartimento di Informatica, Universita di Torino 10149 Torino, Italy; e-mai1:bobbioQdi.unito.it ...
... 3. Numerical techniques in dependability analysis While fault trees are solved by resorting t... more ... 3. Numerical techniques in dependability analysis While fault trees are solved by resorting to combin-atoriai techniques and Boolean algebra, the modell-ing techniques surveyed in 2.1, 2.2 and 2.3 lead to the formulation of stochastic point processes. ...
Page 1. Non-Exponential Stochastic Petri Nets: an Overview of Methods and Techniques PART 1: Sema... more Page 1. Non-Exponential Stochastic Petri Nets: an Overview of Methods and Techniques PART 1: Semantics and Speci cations Andrea Bobbio Dipartimento di Informatica Universit a di Torino, 10149 Torino, Italy PART 2: Solution Techniques ...
... MISC{Bobbio93thetask, author = {Andrea Bobbio and Miklós Telek}, title = {The Task Completion... more ... MISC{Bobbio93thetask, author = {Andrea Bobbio and Miklós Telek}, title = {The Task Completion Time in Degradable Systems}, year = {1993} }. ... 1987. 12, The Completion Time of Programs on Processors Subject to Failure and Repair Chimento, Trivedi - 1993. ...
Reliability and Availability Engineering Modeling, Analysis, and Applications Do you need to know... more Reliability and Availability Engineering Modeling, Analysis, and Applications Do you need to know what technique to use to evaluate the reliability of an engineered system? This self-contained guide provides comprehensive coverage of all the analytical and modeling techniques currently in use, from classical non-state and state space approaches, to newer and more advanced methods such as binary decision diagrams, dynamic fault trees, Bayesian belief networks, stochastic Petri nets, non-homogeneous Markov chains, semi-Markov processes, and phase type expansions. Readers will quickly understand the relative pros and cons of each technique, as well as how to combine different models together to address complex, real-world modeling scenarios. Numerous examples, case studies and problems provided throughout help readers put knowledge into practice, and a solutions manual and Powerpoint slides for instructors accompany the book online. This is the ideal self-study guide for students, researchers and practitioners in engineering and computer science.
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