Input Modeling with Phase-Type Distributions and Markov Models PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Input Modeling with Phase-Type Distributions and Markov Models PDF full book. Access full book title Input Modeling with Phase-Type Distributions and Markov Models by Peter Buchholz. Download full books in PDF and EPUB format.
Author: Peter Buchholz
Publisher: Springer
ISBN: 3319066749
Category : Mathematics
Languages : en
Pages : 137
Get Book Here
Book Description
Containing a summary of several recent results on Markov-based input modeling in a coherent notation, this book introduces and compares algorithms for parameter fitting and gives an overview of available software tools in the area. Due to progress made in recent years with respect to new algorithms to generate PH distributions and Markovian arrival processes from measured data, the models outlined are useful alternatives to other distributions or stochastic processes used for input modeling. Graduate students and researchers in applied probability, operations research and computer science along with practitioners using simulation or analytical models for performance analysis and capacity planning will find the unified notation and up-to-date results presented useful. Input modeling is the key step in model based system analysis to adequately describe the load of a system using stochastic models. The goal of input modeling is to find a stochastic model to describe a sequence of measurements from a real system to model for example the inter-arrival times of packets in a computer network or failure times of components in a manufacturing plant. Typical application areas are performance and dependability analysis of computer systems, communication networks, logistics or manufacturing systems but also the analysis of biological or chemical reaction networks and similar problems. Often the measured values have a high variability and are correlated. It’s been known for a long time that Markov based models like phase type distributions or Markovian arrival processes are very general and allow one to capture even complex behaviors. However, the parameterization of these models results often in a complex and non-linear optimization problem. Only recently, several new results about the modeling capabilities of Markov based models and algorithms to fit the parameters of those models have been published.
Author: Peter Buchholz
Publisher: Springer
ISBN: 3319066749
Category : Mathematics
Languages : en
Pages : 137
Get Book Here
Book Description
Containing a summary of several recent results on Markov-based input modeling in a coherent notation, this book introduces and compares algorithms for parameter fitting and gives an overview of available software tools in the area. Due to progress made in recent years with respect to new algorithms to generate PH distributions and Markovian arrival processes from measured data, the models outlined are useful alternatives to other distributions or stochastic processes used for input modeling. Graduate students and researchers in applied probability, operations research and computer science along with practitioners using simulation or analytical models for performance analysis and capacity planning will find the unified notation and up-to-date results presented useful. Input modeling is the key step in model based system analysis to adequately describe the load of a system using stochastic models. The goal of input modeling is to find a stochastic model to describe a sequence of measurements from a real system to model for example the inter-arrival times of packets in a computer network or failure times of components in a manufacturing plant. Typical application areas are performance and dependability analysis of computer systems, communication networks, logistics or manufacturing systems but also the analysis of biological or chemical reaction networks and similar problems. Often the measured values have a high variability and are correlated. It’s been known for a long time that Markov based models like phase type distributions or Markovian arrival processes are very general and allow one to capture even complex behaviors. However, the parameterization of these models results often in a complex and non-linear optimization problem. Only recently, several new results about the modeling capabilities of Markov based models and algorithms to fit the parameters of those models have been published.
Author: András Horváth
Publisher: John Wiley & Sons
ISBN: 1394329903
Category : Mathematics
Languages : en
Pages : 292
Get Book Here
Book Description
Phase type distributions are widely applicable modeling and statistical tools for non-negative random quantities. They are built on Markov chains, which provide a simple, intuitive stochastic interpretation for their use. Phase Type Distribution starts from the Markov chain-based definition of phase type distributions and presents many interesting properties, which follow from the basic definition. As a general family of non-negative distributions with nice analytical properties, phase type distributions can be used for approximating experimental distributions by fitting or by moments matching; and, for discrete event simulation of real word systems with stochastic timing, such as production systems, service operations, communication networks, etc. This book summarizes the up-to-date fitting, matching and simulation methods, and presents the limits of flexibility of phase type distributions of a given order. Additionally, this book lists numerical examples that support the intuitive understanding of the analytical descriptions and software tools that handle phase type distributions.
Author: Howard M. Taylor
Publisher: Academic Press
ISBN: 1483269272
Category : Mathematics
Languages : en
Pages : 410
Get Book Here
Book Description
An Introduction to Stochastic Modeling provides information pertinent to the standard concepts and methods of stochastic modeling. This book presents the rich diversity of applications of stochastic processes in the sciences. Organized into nine chapters, this book begins with an overview of diverse types of stochastic models, which predicts a set of possible outcomes weighed by their likelihoods or probabilities. This text then provides exercises in the applications of simple stochastic analysis to appropriate problems. Other chapters consider the study of general functions of independent, identically distributed, nonnegative random variables representing the successive intervals between renewals. This book discusses as well the numerous examples of Markov branching processes that arise naturally in various scientific disciplines. The final chapter deals with queueing models, which aid the design process by predicting system performance. This book is a valuable resource for students of engineering and management science. Engineers will also find this book useful.
Author: S?ren Asmussen
Publisher: World Scientific
ISBN: 9814282529
Category : Mathematics
Languages : en
Pages : 621
Get Book Here
Book Description
The book gives a comprehensive treatment of the classical and modern ruin probability theory. Some of the topics are Lundberg's inequality, the Cramr?Lundberg approximation, exact solutions, other approximations (e.g., for heavy-tailed claim size distributions), finite horizon ruin probabilities, extensions of the classical compound Poisson model to allow for reserve-dependent premiums, Markov-modulation, periodicity, change of measure techniques, phase-type distributions as a computational vehicle and the connection to other applied probability areas, like queueing theory. In this substantially updated and extended second version, new topics include stochastic control, fluctuation theory for Levy processes, Gerber?Shiu functions and dependence.
Author: Nikolaos Limnios
Publisher: John Wiley & Sons
ISBN: 1789450373
Category : Social Science
Languages : en
Pages : 338
Get Book Here
Book Description
The study of earthquakes is a multidisciplinary field, an amalgam of geodynamics, mathematics, engineering and more. The overriding commonality between them all is the presence of natural randomness. Stochastic studies (probability, stochastic processes and statistics) can be of different types, for example, the black box approach (one state), the white box approach (multi-state), the simulation of different aspects, and so on. This book has the advantage of bringing together a group of international authors, known for their earthquake-specific approaches, to cover a wide array of these myriad aspects. A variety of topics are presented, including statistical nonparametric and parametric methods, a multi-state system approach, earthquake simulators, post-seismic activity models, time series Markov models with regression, scaling properties and multifractal approaches, selfcorrecting models, the linked stress release model, Markovian arrival models, Poisson-based detection techniques, change point detection techniques on seismicity models, and, finally, semi-Markov models for earthquake forecasting.
Author: Alexander Dudin
Publisher: Springer
ISBN: 3319446150
Category : Computers
Languages : en
Pages : 407
Get Book Here
Book Description
This book constitutes the refereed proceedings of the 15th International Scientific Conference on Information Technologies and Mathematical Modeling, named after A. F. Terpugov, ITMM 2016, held in Katun, Russia, in September 2016. The 33 full papers presented together with 4 short papers were carefully reviewed and selected from 96 submissions. They are devoted to new results in the queueing theory and its applications, addressing specialists in probability theory, random processes, mathematical modeling as well as engineers dealing with logical and technical design and operational management of telecommunication and computer networks.
Author: Jane Hillston
Publisher: Springer Nature
ISBN: 3031684168
Category :
Languages : en
Pages : 352
Get Book Here
Book Description
Author: Kai Fischbach
Publisher: Springer
ISBN: 3319053590
Category : Computers
Languages : en
Pages : 277
Get Book Here
Book Description
This book constitutes the refereed proceedings of the 17th International GI/ITG Conference on Measurement, Modeling and Evaluation of Computing Systems and Dependability and Fault-Tolerance, MMB & DFT 2014, held in Bamberg, Germany, in March 2014. The 21 papers presented (2 invited papers, 3 tool papers and 16 full papers) were carefully reviewed and selected from numerous submissions. MMB & DFT 2014 cover all aspects of performance and dependability evaluation of systems including networks, computer architectures, distributed systems, workflow systems, software, fault-tolerant and secure systems. The conference also featured 3 satellite workshops namely the International Workshop on Demand Modeling and Quantitative Analysis of Future Generation Energy Networks and Energy-Efficient Systems, FGENET 2014; the International Workshop on Modeling, Analysis and Management of Social Networks and their Applications, SOCNET 2014 and the 2nd Workshop on Network Calculus, WoNeCa 2014.
Author: Mariette Awad
Publisher: Apress
ISBN: 1430259906
Category : Computers
Languages : en
Pages : 263
Get Book Here
Book Description
Machine learning techniques provide cost-effective alternatives to traditional methods for extracting underlying relationships between information and data and for predicting future events by processing existing information to train models. Efficient Learning Machines explores the major topics of machine learning, including knowledge discovery, classifications, genetic algorithms, neural networking, kernel methods, and biologically-inspired techniques. Mariette Awad and Rahul Khanna’s synthetic approach weaves together the theoretical exposition, design principles, and practical applications of efficient machine learning. Their experiential emphasis, expressed in their close analysis of sample algorithms throughout the book, aims to equip engineers, students of engineering, and system designers to design and create new and more efficient machine learning systems. Readers of Efficient Learning Machines will learn how to recognize and analyze the problems that machine learning technology can solve for them, how to implement and deploy standard solutions to sample problems, and how to design new systems and solutions. Advances in computing performance, storage, memory, unstructured information retrieval, and cloud computing have coevolved with a new generation of machine learning paradigms and big data analytics, which the authors present in the conceptual context of their traditional precursors. Awad and Khanna explore current developments in the deep learning techniques of deep neural networks, hierarchical temporal memory, and cortical algorithms. Nature suggests sophisticated learning techniques that deploy simple rules to generate highly intelligent and organized behaviors with adaptive, evolutionary, and distributed properties. The authors examine the most popular biologically-inspired algorithms, together with a sample application to distributed datacenter management. They also discuss machine learning techniques for addressing problems of multi-objective optimization in which solutions in real-world systems are constrained and evaluated based on how well they perform with respect to multiple objectives in aggregate. Two chapters on support vector machines and their extensions focus on recent improvements to the classification and regression techniques at the core of machine learning.
Author: Aleksandra Gruca
Publisher: Springer
ISBN: 3319677926
Category : Technology & Engineering
Languages : en
Pages : 599
Get Book Here
Book Description
This Proceedings book provides essential insights into the current state of research in the field of human–computer interactions. It presents the outcomes of the International Conference on Man–Machine Interactions (ICMMI 2017), held on October 3–6, 2017, in Cracow, Poland, which offers a unique international platform for researchers and practitioners to share cutting-edge developments related to technologies, algorithms, tools and systems focused on the means by which humans interact and communicate with computers. This book is the 5th edition in the series and includes a unique selection of high-quality, original papers highlighting the latest theoretical and practical research on technologies, applications and challenges encountered in the rapidly evolving new forms of human–machine relationships. Major research topics covered include human–computer interfaces, bio-data analysis and mining, image analysis and signal processing, decision support and expert systems, pattern recognition, algorithms and optimisations, computer networks, and data management systems. As such, the book offers a valuable resource for researchers in academia, industry and other fields whose work involves man–machine interactions.
