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Paul Gagniuc A. Markov Chains. From Theory to Implementation and Experimentation


A fascinating and instructive guide to Markov chains for experienced users and newcomers alike This unique guide to Markov chains approaches the subject along the four convergent lines of mathematics, implementation, simulation, and experimentation. It introduces readers to the art of stochastic modeling, shows how to design computer implementations, and provides extensive worked examples with case studies. Markov Chains: From Theory to Implementation and Experimentation begins with a general introduction to the history of probability theory in which the author uses quantifiable examples to illustrate how probability theory arrived at the concept of discrete-time and the Markov model from experiments involving independent variables. An introduction to simple stochastic matrices and transition probabilities is followed by a simulation of a two-state Markov chain. The notion of steady state is explored in connection with the long-run distribution behavior of the Markov chain. Predictions based on Markov chains with more than two states are examined, followed by a discussion of the notion of absorbing Markov chains. Also covered in detail are topics relating to the average time spent in a state, various chain configurations, and n-state Markov chain simulations used for verifying experiments involving various diagram configurations. • Fascinating historical notes shed light on the key ideas that led to the development of the Markov model and its variants • Various configurations of Markov Chains and their limitations are explored at length • Numerous examples—from basic to complex—are presented in a comparative manner using a variety of color graphics • All algorithms presented can be analyzed in either Visual Basic, Java Script, or PHP • Designed to be useful to professional statisticians as well as readers without extensive knowledge of probability theory Covering both the theory underlying the Markov model and an array of Markov chain implementations, within a common conceptual framework, Markov Chains: From Theory to Implementation and Experimentation is a stimulating introduction to and a valuable reference for those wishing to deepen their understanding of this extremely valuable statistical tool. Paul A. Gagniuc, PhD, is Associate Professor at Polytechnic University of Bucharest, Romania. He obtained his MS and his PhD in genetics at the University of Bucharest. Dr. Gagniuc’s work has been published in numerous high profile scientific journals, ranging from the Public Library of Science to BioMed Central and Nature journals. He is the recipient of several awards for exceptional scientific results and a highly active figure in the review process for different scientific areas.

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Bruno Sericola Markov Chains. Theory and Applications


Markov chains are a fundamental class of stochastic processes. They are widely used to solve problems in a large number of domains such as operational research, computer science, communication networks and manufacturing systems. The success of Markov chains is mainly due to their simplicity of use, the large number of available theoretical results and the quality of algorithms developed for the numerical evaluation of many metrics of interest. The author presents the theory of both discrete-time and continuous-time homogeneous Markov chains. He carefully examines the explosion phenomenon, the Kolmogorov equations, the convergence to equilibrium and the passage time distributions to a state and to a subset of states. These results are applied to birth-and-death processes. He then proposes a detailed study of the uniformization technique by means of Banach algebra. This technique is used for the transient analysis of several queuing systems. Contents 1. Discrete-Time Markov Chains 2. Continuous-Time Markov Chains 3. Birth-and-Death Processes 4. Uniformization 5. Queues About the Authors Bruno Sericola is a Senior Research Scientist at Inria Rennes – Bretagne Atlantique in France. His main research activity is in performance evaluation of computer and communication systems, dependability analysis of fault-tolerant systems and stochastic models.

11008.48 РУБ

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Gernot A. Fink Markov Models for Pattern Recognition. From Theory to Applications


Книга "Markov Models for Pattern Recognition. From Theory to Applications".

9152 РУБ

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Carl Graham Markov Chains. Analytic and Monte Carlo Computations


Markov Chains: Analytic and Monte Carlo Computations introduces the main notions related to Markov chains and provides explanations on how to characterize, simulate, and recognize them. Starting with basic notions, this book leads progressively to advanced and recent topics in the field, allowing the reader to master the main aspects of the classical theory. This book also features: Numerous exercises with solutions as well as extended case studies. A detailed and rigorous presentation of Markov chains with discrete time and state space. An appendix presenting probabilistic notions that are necessary to the reader, as well as giving more advanced measure-theoretic notions.

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Poggiolini Laura A First Course in Probability and Markov Chains


Provides an introduction to basic structures of probability with a view towards applications in information technology A First Course in Probability and Markov Chains presents an introduction to the basic elements in probability and focuses on two main areas. The first part explores notions and structures in probability, including combinatorics, probability measures, probability distributions, conditional probability, inclusion-exclusion formulas, random variables, dispersion indexes, independent random variables as well as weak and strong laws of large numbers and central limit theorem. In the second part of the book, focus is given to Discrete Time Discrete Markov Chains which is addressed together with an introduction to Poisson processes and Continuous Time Discrete Markov Chains. This book also looks at making use of measure theory notations that unify all the presentation, in particular avoiding the separate treatment of continuous and discrete distributions. A First Course in Probability and Markov Chains: Presents the basic elements of probability. Explores elementary probability with combinatorics, uniform probability, the inclusion-exclusion principle, independence and convergence of random variables. Features applications of Law of Large Numbers. Introduces Bernoulli and Poisson processes as well as discrete and continuous time Markov Chains with discrete states. Includes illustrations and examples throughout, along with solutions to problems featured in this book. The authors present a unified and comprehensive overview of probability and Markov Chains aimed at educating engineers working with probability and statistics as well as advanced undergraduate students in sciences and engineering with a basic background in mathematical analysis and linear algebra.

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K Borovkov Elements of Stochastic Modelling


This textbook has been developed from the lecture notes for a one-semester course on stochastic modelling. It reviews the basics of probability theory and then covers the following topics: Markov chains, Markov decision processes, jump Markov processes, elements of queueing theory, basic renewal theory, elements of time series and simulation. Rigorous proofs are often replaced with sketches of arguments — with indications as to why a particular result holds, and also how it is connected with other results — and illustrated by examples. Wherever possible, the book includes references to more specialised texts containing both proofs and more advanced material related to the topics covered.

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Jianjun Paul Tian Evolution Algebras and their Applications


The author explores evolution algebras, which lie between algebras and dynamical systems. Readers learn the foundations of evolution algebras theory and its applications in non-Mendelian genetics and Markov chains. They'll also discover evolution algebras' connections with other mathematical fields, including graph theory, group theory, stochastic processes, dynamical systems, knot theory, 3-manifolds, and the Ihara-Selberg zeta function.

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Leonid Koralov, Yakov G. Sinai Theory of Probability and Random Processes


A one-year course in probability theory and the theory of random processes, taught at Princeton University to undergraduate and graduate students, forms the core of this book. It provides a comprehensive and self-contained exposition of classical probability theory and the theory of random processes. The book includes detailed discussion of Lebesgue integration, Markov chains, random walks, laws of large numbers, limit theorems, and their relation to Renormalization Group theory. It also includes the theory of stationary random processes, martingales, generalized random processes, and Brownian motion.

6502 РУБ

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Moyal Pascal Stochastic Modeling and Analysis of Telecom Networks


This book addresses the stochastic modeling of telecommunication networks, introducing the main mathematical tools for that purpose, such as Markov processes, real and spatial point processes and stochastic recursions, and presenting a wide list of results on stability, performances and comparison of systems. The authors propose a comprehensive mathematical construction of the foundations of stochastic network theory: Markov chains, continuous time Markov chains are extensively studied using an original martingale-based approach. A complete presentation of stochastic recursions from an ergodic theoretical perspective is also provided, as well as spatial point processes. Using these basic tools, stability criteria, performance measures and comparison principles are obtained for a wide class of models, from the canonical M/M/1 and G/G/1 queues to more sophisticated systems, including the current “hot topics” of spatial radio networking, OFDMA and real-time networks. Contents 1. Introduction. Part 1: Discrete-time Modeling 2. Stochastic Recursive Sequences. 3. Markov Chains. 4. Stationary Queues. 5. The M/GI/1 Queue. Part 2: Continuous-time Modeling 6. Poisson Process. 7. Markov Process. 8. Systems with Delay. 9. Loss Systems. Part 3: Spatial Modeling 10. Spatial Point Processes.

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Konstantin Borovkov Elements of Stochastic Modelling. 2nd Edition


This is the expanded second edition of a successful textbook that provides a broad introduction to important areas of stochastic modelling. The original text was developed from lecture notes for a one-semester course for third-year science and actuarial students at the University of Melbourne. It reviewed the basics of probability theory and then covered the following topics: Markov chains, Markov decision processes, jump Markov processes, elements of queueing theory, basic renewal theory, elements of time series and simulation.The present edition adds new chapters on elements of stochastic calculus and introductory mathematical finance that logically complement the topics chosen for the first edition. This makes the book suitable for a larger variety of university courses presenting the fundamentals of modern stochastic modelling. Instead of rigorous proofs we often give only sketches of the arguments, with indications as to why a particular result holds and also how it is related to other results, and illustrate them by examples. Wherever possible, the book includes references to more specialised texts on respective topics that contain both proofs and more advanced material.

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Ionut Florescu Probability and Stochastic Processes


A comprehensive and accessible presentation of probability and stochastic processes with emphasis on key theoretical concepts and real-world applications With a sophisticated approach, Probability and Stochastic Processes successfully balances theory and applications in a pedagogical and accessible format. The book’s primary focus is on key theoretical notions in probability to provide a foundation for understanding concepts and examples related to stochastic processes. Organized into two main sections, the book begins by developing probability theory with topical coverage on probability measure; random variables; integration theory; product spaces, conditional distribution, and conditional expectations; and limit theorems. The second part explores stochastic processes and related concepts including the Poisson process, renewal processes, Markov chains, semi-Markov processes, martingales, and Brownian motion. Featuring a logical combination of traditional and complex theories as well as practices, Probability and Stochastic Processes also includes: Multiple examples from disciplines such as business, mathematical finance, and engineering Chapter-by-chapter exercises and examples to allow readers to test their comprehension of the presented material A rigorous treatment of all probability and stochastic processes concepts An appropriate textbook for probability and stochastic processes courses at the upper-undergraduate and graduate level in mathematics, business, and electrical engineering, Probability and Stochastic Processes is also an ideal reference for researchers and practitioners in the fields of mathematics, engineering, and finance.

9603.03 РУБ

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Daniel W. Stroock Markov Processes from K. Itos Perspective (AM-155)


Книга "Markov Processes from K. Itô's Perspective (AM-155)".Kiyosi Itô's greatest contribution to probability theory may be his introduction of stochastic differential equations to explain the Kolmogorov-Feller theory of Markov processes. Starting with the geometric ideas that guided him, this book gives an account of Itô's program. The modern theory of Markov processes was initiated by A. N. Kolmogorov. However, Kolmogorov's approach was too analytic to reveal the probabilistic foundations on which it rests. In particular, it hides the central role played by the simplest Markov processes: those with independent, identically distributed increments. To remedy this defect, Itô interpreted Kolmogorov's famous forward equation as an equation that describes the integral curve of a vector field on the space of probability measures. Thus, in order to show how Itô's thinking leads to his theory of stochastic integral equations, Stroock begins with an account of integral curves on the space of probability measures and then arrives at stochastic integral equations when he moves to a pathspace setting. In the first half of the book, everything is done in the context of general independent increment processes and without explicit use of Itô's stochastic integral calculus. In the second half, the author provides a systematic development of Itô's theory of stochastic integration: first for Brownian motion and then for continuous martingales. The fina...

9427 РУБ

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Andreas E. Kyprianou Fluctuations of Levy Processes with Applications. Introductory Lectures


Lévy processes are the natural continuous-time analogue of random walks and form a rich class of stochastic processes around which a robust mathematical theory exists. Their application appears in the theory of many areas of classical and modern stochastic processes including storage models, renewal processes, insurance risk models, optimal stopping problems, mathematical finance, continuous-state branching processes and positive self-similar Markov processes.This textbook is based on a series of graduate courses concerning the theory and application of Lévy processes from the perspective of their path fluctuations. Central to the presentation is the decomposition of paths in terms of excursions from the running maximum as well as an understanding of short- and long-term behaviour.The book aims to be mathematically rigorous while still providing an intuitive feel for underlying principles. The results and applications often focus on the case of Lévy processes with jumps in only one direction, for which recent theoretical advances have yielded a higher degree of mathematical tractability.The second edition additionally addresses recent developments in the potential analysis of subordinators, Wiener-Hopf theory, the theory of scale functions and their application to ruin theory, as well as including an extensive overview of the classical and modern theory of positive self-similar Markov processes. Each chapter has a comprehensive set of exercises.

9802 РУБ

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Mary Ochieng Application of Markov Chain and Network theory


Labor is one of the key resources in any institution that if managed well can lead to increased productivity. Regular evaluation of existing structures and policies is necessary to avoid wastage and stagnation. A lot of work has been done in analysis of manpower systems using Markov models and other models such as computer simulation models, optimization models, supply chain models and colonic models. In this study Markov Chain theory has been integrated with network theory in evaluating effective administration of the current schemes of service for teachers by the Teachers Service Commission in Kenya. Ecosystem indicators applicable to a hierarchical system are used to assess the growth and development of the teacher progression system.Markov chain theory facilitates computation of the elements of the export compartments of the flow matrix while network theory goes beneath the surface to reveal features about the manpower system that arise from its underlying structure.

3944 РУБ

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William Wilson Holographic Data Storage. From Theory to Practical Systems


Holographic Data Storage: From Theory to Practical Systems is a primer on the design and building of a holographic data storage system covering the physics, Servo, Data Channel, Recording Materials, and optics behind holographic storage, the requirements of a functioning system, and its integration into «real-life» systems. Later chapters highlight recent developments in holographic storage which have enabled readiness for commercial implementation and discuss the general outlook for the technology, including the transition from professional to consumer markets and the possibilities for mass reproduction.

13521.51 РУБ

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Rogers Patricia J. Purposeful Program Theory. Effective Use of Theories Change and Logic Models


Between good intentions and great results lies a program theory—not just a list of tasks but a vision of what needs to happen, and how. Now widely used in government and not-for-profit organizations, program theory provides a coherent picture of how change occurs and how to improve performance. Purposeful Program Theory shows how to develop, represent, and use program theory thoughtfully and strategically to suit your particular situation, drawing on the fifty-year history of program theory and the authors experiences over more than twenty-five years. «From needs assessment to intervention design, from implementation to outcomes evaluation, from policy formulation to policy execution and evaluation, program theory is paramount. But until now no book has examined these multiple uses of program theory in a comprehensive, understandable, and integrated way. This promises to be a breakthrough book, valuable to practitioners, program designers, evaluators, policy analysts, funders, and scholars who care about understanding why an intervention works or doesnt work.» —Michael Quinn Patton, author, Utilization-Focused Evaluation «Finally, the definitive guide to evaluation using program theory! Far from the narrow one true way approaches to program theory, this book provides numerous practical options for applying program theory to fulfill different purposes and constraints, and guides the reader through the sound critical thinking required to select from among the options. The tour de force of the history and use of program theory is a truly global view, with examples from around the world and across the full range of content domains. A must-have for any serious evaluator.» —E. Jane Davidson, PhD, Real Evaluation Ltd. Companion Web site: josseybass.com/go/funnellrogers

7127.79 РУБ

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Thomas Augustin Introduction to Imprecise Probabilities


In recent years, the theory has become widely accepted and has been further developed, but a detailed introduction is needed in order to make the material available and accessible to a wide audience. This will be the first book providing such an introduction, covering core theory and recent developments which can be applied to many application areas. All authors of individual chapters are leading researchers on the specific topics, assuring high quality and up-to-date contents. An Introduction to Imprecise Probabilities provides a comprehensive introduction to imprecise probabilities, including theory and applications reflecting the current state if the art. Each chapter is written by experts on the respective topics, including: Sets of desirable gambles; Coherent lower (conditional) previsions; Special cases and links to literature; Decision making; Graphical models; Classification; Reliability and risk assessment; Statistical inference; Structural judgments; Aspects of implementation (including elicitation and computation); Models in finance; Game-theoretic probability; Stochastic processes (including Markov chains); Engineering applications. Essential reading for researchers in academia, research institutes and other organizations, as well as practitioners engaged in areas such as risk analysis and engineering.

9141.65 РУБ

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M.L. Silverstein Boundary Theory for Symmetric Markov Processes


Книга "Boundary Theory for Symmetric Markov Processes".

7702 РУБ

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Romain Azais Statistical Inference for Piecewise-deterministic Markov Processes


Piecewise-deterministic Markov processes form a class of stochastic models with a sizeable scope of applications: biology, insurance, neuroscience, networks, finance… Such processes are defined by a deterministic motion punctuated by random jumps at random times, and offer simple yet challenging models to study. Nevertheless, the issue of statistical estimation of the parameters ruling the jump mechanism is far from trivial. Responding to new developments in the field as well as to current research interests and needs, Statistical inference for piecewise-deterministic Markov processes offers a detailed and comprehensive survey of state-of-the-art results. It covers a wide range of general processes as well as applied models. The present book also dwells on statistics in the context of Markov chains, since piecewise-deterministic Markov processes are characterized by an embedded Markov chain corresponding to the position of the process right after the jumps.

9569.72 РУБ

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Georg Baltes New Perspectives on Supply and Distribution Chain Financing. Case Studies from China Europe


Globalization no longer means simply finding low-cost countries for sourcing, but has involved to include the opportunity for growth in Asia's emerging domestic markets, specifically China. This development results in extended, truly global supply chains and thus places a higher pressure on working capital. Therefore, several definitions of Supply Chain Management already encompass financial aspects and demand a more integrated consideration of material, information, and financial flows within supply chains. However, more precise theory on "Supply Chain Financing" is understudied and initial implementation of related solutions in industry has only gained momentum during and after the economic and financial crises of 2008 and 2009.In contrast to traditional financial instruments for supply chains - for instance trade finance products which have been around for more than a century - Supply Chain Financing leverages larger and influential members of supply chains. These firms might, for instance, provide easier and cheaper access to financing for smaller supply chain members supporting their profitability through renegotiated and reduced purchasing prices. Echoing recent research results on supply chain risk management, other firms may prefer to take on a supply chain perspective and work on creating agile and resilient supply chains. In this context, Supply Chain Financing can be employed to ensure liquidity for crucial upstream and downstream supply chain partners ...

9439 РУБ

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