Last edited by Voodoozahn

Sunday, October 18, 2020 | History

7 edition of **Regenerative stochastic simulation** found in the catalog.

- 368 Want to read
- 12 Currently reading

Published
**1993**
by Academic Press in Boston
.

Written in English

- Simulation methods.,
- Stochastic processes.,
- Decision making -- Statistical methods.

**Edition Notes**

Includes bibliographical references (p. 385-392) and index.

Statement | Gerald S. Shedler. |

Series | Statistical modeling and decision science |

Classifications | |
---|---|

LC Classifications | T57.62 .S475 1993 |

The Physical Object | |

Pagination | ix, 400 p. : |

Number of Pages | 400 |

ID Numbers | |

Open Library | OL1720022M |

ISBN 10 | 0126393605 |

LC Control Number | 92023205 |

Stochastic processes are mathematical models of random phenomena that evolve according to prescribed dynamics. Processes commonly used in applications are Markov chains in discrete and continuous time, renewal and regenerative processes, Poisson processes, and Brownian motion. This volume gives an in-depth description of the structure and basic properties of these stochastic . CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): We present a regenerative simulation method applicable to a special class of non-Markovian stochastic Petri net (SPN) that may be useful in practice for constructing and solving performability models. Introduced in [1], this special SPN is called a phased delay Petri net (PDPN) and was conceived to allow efficient.

Written by a leading researcher this book presents an introduction to Stochastic Petri Nets covering the modeling power of the proposed SPN model, the stability conditions and the simulation methods. Its unique and well-written approach provides a timely and important addition to the : $ We present a regenerative simulation method applica-ble to a special class of non-Markovian stochastic Petri net (SPN) that may be useful in practice for constructing and solving performability models. Introduced in [1], this spe-cial SPN is called a phased delay Petri net (PDPN) and was conceived to allow efﬁcient numerical analysis while.

Zheng Z and Glynn P Extensions of the regenerative method to new functionals Proceedings of the Winter Simulation Conference, () Bayer F, Lorenzen M, Müller M and Allgöwer F () Robust economic Model Predictive Control using stochastic information, Automatica (Journal of IFAC), C, (), Online publication date: 1-Dec. Stochastic Simulation: Algorithms and Analysis (Stochastic Modelling and Applied Probability, ) Søren Asmussen, Peter W. Glynn, Sampling-based computational methods have become a fundamental part of the numerical toolset of practitioners and researchers across an enormous.

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The focus of this book is on simulations of discrete-event stochastic systems; namely, simulations in which stochastic state transitions occur only at an increasing sequence of random times. The discussion emphasizes simulations. Regenerative Stochastic Simulation by Shedler, G.S. and a great selection of related books, art and collectibles available now at - Regenerative Stochastic Simulation Statistical Modeling and Decision Science by Shedler, Gerald S - AbeBooks.

Introduction Regenerative simulation refers to a collection of statistical techniques for analyzing the output of a discrete-event stochastic simulation whose underlying stochastic process { X (t): t ≥ 0 } This is a preview of subscription content, log in to check access.

A regenerative stochastic process has the characteristic property that there exists an infinite sequence of random times at which the process probabilistically restarts. As discussed in Sectionthe essence of regeneration is that the evolution of the process between any two successive regeneration points is an independent Regenerative stochastic simulation book.

Stochastic simulation: its seed virtually precludes the application of regenerative processes 1 July | Advances in Applied Probability, Vol. 12, No. 2 Operation of merge junctions in a dynamically entrained automated guideway transit systemCited by: Sampling-based computational methods have become a fundamental part of the numerical toolset of practitioners and researchers across an enormous number of different applied domains and academic disciplines.

This book provides a broad treatment of such sampling-based methods, as well as accompanying mathematical analysis of the convergence properties of the methods discussed.3/5(1). Download Stochastic Processes Modeling And Simulation books, This sequel to volume 19 of Handbook on Statistics on Stochastic Processes: Modelling and Simulation is concerned mainly with the theme of reviewing and, in some cases, unifying with new ideas the different lines of research and developments in stochastic processes of applied flavour.

This sequel to volume 19 of Handbook on Statistics on Stochastic Processes: Modelling and Simulation is concerned mainly with the theme of reviewing and, in some cases, unifying with new ideas the different lines of research and developments in stochastic processes of applied flavour.

This volume consists of 23 chapters addressing various topics in stochastic processes. Stochastic Simulation: Algorithms and Analysis et Springer. Contents Preface v Notation xii I What This Book Is About 1 1 An Illustrative Example: The Single-Server Queue 1 2 The Monte Carlo Method 5 4 The Regenerative Method 5 The Method of Batch Means The regenerative method possesses certain asymptotic properties that dominate those of other steady-state simulation output analysis methods, such as batch means.

Therefore, applying the regenerative method to steady-state discrete-event system simulations is of great interest. In this paper, we survey the state of the art in this area.

The book is an introduction to stochastic processes with applications from physics and finance. It introduces the basic notions of probability theory and the mathematics of stochastic processes. The applications that we discuss are chosen to show the interdisciplinary character of the concepts and methods and are taken from physics and finance.

Regenerative Simulation. Pages Haas, Peter J. Preview Buy Chap95 € Alternative Simulation Methods. Pages Book Title Stochastic Petri Nets Book Subtitle Modelling, Stability, Simulation Authors.

Peter J. Haas; Series Title. Examples include the estimation of a steady-state parameter α = ER/Eτ for a regenerative stochastic process, where τ denotes the length of a regenerative cycle and R denotes the cumulative.

Stochastic Modeling: Analysis and Simulation - Ebook written by Barry L. Nelson. Read this book using Google Play Books app on your PC, android, iOS devices.

Download for offline reading, highlight, bookmark or take notes while you read Stochastic Modeling: Analysis and Simulation.

Regenerative Stochastic Processes The Regenerative Property Limit Theorems Regenerative Generalized Semi-Markov Processes Regenerative Simulation The Regenerative Method Implementation Considerations Discrete Time Conversion Theoretical Values for Markov Claims Statistical Efficiency A standard strategy in simulation, for comparing two stochastic systems, is to use a common sequence of random numbers to drive both systems.

Since re. The regenerative method for simulations of stochastic systems allows data collection at the entry times to a single recurrent state of the process of interest.

Estimates of estimator variance are then easily computed since the generated observations have the desirable property of being independent and identically distributed.

Regenerative Simulation of Stochastic Petri Nets with Discrete and Continuous Timing Article (PDF Available) October with 39 Reads How we measure 'reads'. Stochastic processes are mathematical models of random phenomena that evolve according to prescribed dynamics.

Processes commonly used in applications are Markov chains in discrete and continuous time, renewal and regenerative processes, Poisson processes, and.

Regenerative Method for Simulation Analysis. Reuven Y. Rubinstein. Technion, Israel Institute of Technology. Search for more papers by this author. Book Author(s): Reuven Y. Rubinstein. Technion, Israel Institute of Technology. Selecting the Best Stable Stochastic System.

The Regenerative Method for constrained Optimization Problems. [66] L., Malhis and W., Sanders, “ An efficient two-stage iterative method for the steady-state analysis of Markov regenerative stochastic Petri net models, ” Performance Evaluation, vol.

.“This book is an introduction to applied stochastic processes written as a text that balances between an introduction focusing on the basics of applied stochastic processes and an advanced text that includes more theoretical aspects of these processes. .Stochastic Simulation: Algorithms and Analysis by Asmussen, Soren available in Hardcover onalso read synopsis and reviews.

This book provides a broad treatment of sampling-based computational methods, as well as.