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Moyal Pascal Stochastic Modeling and Analysis - xn--80aahjaaawyx4aeby6msb.xn--p1ai
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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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Society Actuaries of Introductory Stochastic Analysis for Finance and Insurance


Incorporates the many tools needed for modeling and pricing in finance and insurance Introductory Stochastic Analysis for Finance and Insurance introduces readers to the topics needed to master and use basic stochastic analysis techniques for mathematical finance. The author presents the theories of stochastic processes and stochastic calculus and provides the necessary tools for modeling and pricing in finance and insurance. Practical in focus, the book's emphasis is on application, intuition, and computation, rather than theory. Consequently, the text is of interest to graduate students, researchers, and practitioners interested in these areas. While the text is self-contained, an introductory course in probability theory is beneficial to prospective readers. This book evolved from the author's experience as an instructor and has been thoroughly classroom-tested. Following an introduction, the author sets forth the fundamental information and tools needed by researchers and practitioners working in the financial and insurance industries: * Overview of Probability Theory * Discrete-Time stochastic processes * Continuous-time stochastic processes * Stochastic calculus: basic topics The final two chapters, Stochastic Calculus: Advanced Topics and Applications in Insurance, are devoted to more advanced topics. Readers learn the Feynman-Kac formula, the Girsanov's theorem, and complex barrier hitting times distributions. Finally, readers discover how stochastic analysis and principles are applied in practice through two insurance examples: valuation of equity-linked annuities under a stochastic interest rate environment and calculation of reserves for universal life insurance. Throughout the text, figures and tables are used to help simplify complex theory and pro-cesses. An extensive bibliography opens up additional avenues of research to specialized topics. Ideal for upper-level undergraduate and graduate students, this text is recommended for one-semester courses in stochastic finance and calculus. It is also recommended as a study guide for professionals taking Causality Actuarial Society (CAS) and Society of Actuaries (SOA) actuarial examinations.

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Xavier Descombes Stochastic Geometry for Image Analysis


This book develops the stochastic geometry framework for image analysis purpose. Two main frameworks are described: marked point process and random closed sets models. We derive the main issues for defining an appropriate model. The algorithms for sampling and optimizing the models as well as for estimating parameters are reviewed. Numerous applications, covering remote sensing images, biological and medical imaging, are detailed. This book provides all the necessary tools for developing an image analysis application based on modern stochastic modeling.

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Fabrizio Ruggeri Bayesian Analysis of Stochastic Process Models


Bayesian analysis of complex models based on stochastic processes has in recent years become a growing area. This book provides a unified treatment of Bayesian analysis of models based on stochastic processes, covering the main classes of stochastic processing including modeling, computational, inference, forecasting, decision making and important applied models. Key features: Explores Bayesian analysis of models based on stochastic processes, providing a unified treatment. Provides a thorough introduction for research students. Computational tools to deal with complex problems are illustrated along with real life case studies Looks at inference, prediction and decision making. Researchers, graduate and advanced undergraduate students interested in stochastic processes in fields such as statistics, operations research (OR), engineering, finance, economics, computer science and Bayesian analysis will benefit from reading this book. With numerous applications included, practitioners of OR, stochastic modelling and applied statistics will also find this book useful.

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Nikolaos Limnios Earthquake Statistical Analysis through Multi-state Modeling


Earthquake occurrence modeling is a rapidly developing research area. This book deals with its critical issues, ranging from theoretical advances to practical applications. The introductory chapter outlines state-of-the-art earthquake modeling approaches based on stochastic models. Chapter 2 presents seismogenesis in association with the evolving stress field. Chapters 3 to 5 present earthquake occurrence modeling by means of hidden (semi-)Markov models and discuss associated characteristic measures and relative estimation aspects. Further comparisons, the most important results and our concluding remarks are provided in Chapters 6 and 7.

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Tom Lassen Fatigue Life Analyses of Welded Structures


Avoiding or controlling fatigue damage is a major issue in the design and inspection of welded structures subjected to dynamic loading. Life predictions are usually used for safe life analysis, i.e. for verifying that it is very unlikely that fatigue damage will occur during the target service life of a structure. Damage tolerance analysis is used for predicting the behavior of a fatigue crack and for planning of in-service scheduled inspections. It should be a high probability that any cracks appearing are detected and repaired before they become critical. In both safe life analysis and the damage tolerance analysis there may be large uncertainties involved that have to be treated in a logical and consistent manner by stochastic modeling. This book focuses on fatigue life predictions and damage tolerance analysis of welded joints and is divided into three parts. The first part outlines the common practice used for safe life and damage tolerance analysis with reference to rules and regulations. The second part emphasises stochastic modeling and decision-making under uncertainty, while the final part is devoted to recent advances within fatigue research on welded joints. Industrial examples that are included are mainly dealing with offshore steel structures. Spreadsheets which accompany the book give the reader the possibility for hands-on experience of fatigue life predictions, crack growth analysis and inspection planning. As such, these different areas will be of use to engineers and researchers.

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Ravi R. Mazumdar Performance Modeling, Stochastic Networks, and Statistical Multiplexing

Edward Bodmer Corporate and Project Finance Modeling. Theory and Practice


A clear and comprehensive guide to financial modeling and valuation with extensive case studies and practice exercises Corporate and Project Finance Modeling takes a clear, coherent approach to a complex and technical topic. Written by a globally-recognized financial and economic consultant, this book provides a thorough explanation of financial modeling and analysis while describing the practical application of newly-developed techniques. Theoretical discussion, case studies and step-by-step guides allow readers to master many difficult modeling problems and also explain how to build highly structured models from the ground up. The companion website includes downloadable examples, templates, and hundreds of exercises that allow readers to immediately apply the complex ideas discussed. Financial valuation is an in-depth process, involving both objective and subjective parameters. Precise modeling is critical, and thorough, accurate analysis is what bridges the gap from model to value. This book allows readers to gain a true mastery of the principles underlying financial modeling and valuation by helping them to: Develop flexible and accurate valuation analysis incorporating cash flow waterfalls, depreciation and retirements, updates for new historic periods, and dynamic presentation of scenario and sensitivity analysis; Build customized spreadsheet functions that solve circular logic arising in project and corporate valuation without cumbersome copy and paste macros; Derive accurate measures of normalized cash flow and implied valuation multiples that account for asset life, changing growth, taxes, varying returns and cost of capital; Incorporate stochastic analysis with alternative time series equations and Monte Carlo simulation without add-ins; Understand valuation effects of debt sizing, sculpting, project funding, re-financing, holding periods and credit enhancements. Corporate and Project Finance Modeling provides comprehensive guidance and extensive explanation, making it essential reading for anyone in the field.

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Nikolaos Limnios Mathematical Methods in Survival Analysis, Reliability and Quality of Life


Reliability and survival analysis are important applications of stochastic mathematics (probability, statistics and stochastic processes) that are usually covered separately in spite of the similarity of the involved mathematical theory. This title aims to redress this situation: it includes 21 chapters divided into four parts: Survival analysis, Reliability, Quality of life, and Related topics. Many of these chapters were presented at the European Seminar on Mathematical Methods for Survival Analysis, Reliability and Quality of Life in 2006.

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Vigirdas Mackevicius Introduction to Stochastic Analysis. Integrals and Differential Equations


This is an introduction to stochastic integration and stochastic differential equations written in an understandable way for a wide audience, from students of mathematics to practitioners in biology, chemistry, physics, and finances. The presentation is based on the naïve stochastic integration, rather than on abstract theories of measure and stochastic processes. The proofs are rather simple for practitioners and, at the same time, rather rigorous for mathematicians. Detailed application examples in natural sciences and finance are presented. Much attention is paid to simulation diffusion processes. The topics covered include Brownian motion; motivation of stochastic models with Brownian motion; Itô and Stratonovich stochastic integrals, Itô’s formula; stochastic differential equations (SDEs); solutions of SDEs as Markov processes; application examples in physical sciences and finance; simulation of solutions of SDEs (strong and weak approximations). Exercises with hints and/or solutions are also provided.

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Moyal Pascal - univ-lorraine.fr

Stochastic averaging methods for approximating large state-space processes (with O. Perry (Northwestern Univ.)). Institut Elie Cartan de Lorraine UMR 7502 Université de Lorraine, Site de Nancy B.P. 70239, F-54506 Vandoeuvre-lès-Nancy Cedex FRANCE Office 314 Tel. +33 (0)3 72 74 54 29 Mail : pascal (dot) moyal (at) univ ...

Stochastic Modeling and Analysis of Telecoms Networks ...

Stochastic Modeling and Analysis of Telecoms Networks | Laurent Decreusefond, Pascal Moyal | ISBN: 9781848212381 | Kostenloser Versand für alle Bücher mit Versand und Verkauf duch Amazon.

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Moyal Pascal - univ-lorraine.fr

Pascal Moyal - Teaching Contact; CV; Publications ; Teaching Teaching at Université de Lorraine (2018-Now) Financial Mathematics (Second year graduate - Fall 2018) Stochastic modeling for finance (Second year graduate - Fall 2018) Applied Linear Algebra and algorithms (First year graduate - Fall 2018) Probability Theory (Second year undergraduate - Spring 2019) Teaching at UTC (2007-2014 ...

Stochastic Modeling and Analysis of Telecoms Networks ...

Laurent Decreusefond, Pascal Moyal 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.

Pascal MOYAL | Professor (Full) | Professor | University ...

Pascal Moyal Earliest-Deadline-First is known to be the optimal service discipline to guarantee time deadlines. We investigate here the performance of a single server queue under this discipline.

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Pascal Moyal - The Mathematics Genealogy Project

Pascal Moyal . MathSciNet. Ph.D. Télécom ParisTech 2005. Dissertation: Queueing Systems with Impatient Customers. Mathematics Subject Classification: 60—Probability theory and stochastic processes. Advisor: Laurent Decreusefond. No students known. If you have additional information or corrections regarding this mathematician, please use the update form. To submit students of this ...

Электронная книга: Moyal Pascal. Stochastic Modeling and ...

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 ...

dblp: Pascal Moyal

List of computer science publications by Pascal Moyal In view of the current Corona Virus epidemic, Schloss Dagstuhl has moved its 2020 proposal submission period to July 1 to July 15, 2020 , and there will not be another proposal round in November 2020.

Pascal Moyal - Home

Pascal Moyal. Search for Pascal Moyal's work. Search Search. Home Pascal Moyal. Pascal Moyal. Skip slideshow. Most frequent co-Author ...

Mairesse , Moyal : Stability of the stochastic matching model

On the instability of matching queues Moyal, Pascal and Perry, Ohad, Annals of Applied Probability, 2017; On the dynamic control of matching queues Gurvich, Itai and Ward, Amy, Stochastic Systems, 2014; On deciding stability of multiclass queueing networks under buffer priority scheduling policies Gamarnik, David and Katz, Dmitriy, Annals of Applied Probability, 2009; Critically loaded ...

Stationarity of pure delay systems and queues with ...

Moyal, Pascal Abstract In this paper we solve a particular stochastic recursion in the stationary ergodic framework, and propose some applications of this result to the study of regenerativity (that is, finiteness of busy cycles) and stationarity of some queueing systems: pure delay systems, in which all customers are immediately served, and queues with impatient customers.

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Get this from a library! Stochastic modeling and analysis of telecom networks. [Laurent Decreusefond; Pascal Moyal; N Limnios] -- 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 ...

Stability of the stochastic matching model | Journal of ...

Stability of the stochastic matching model - Volume 53 Issue 4. Skip to main content Accessibility help We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings. ...

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[1907.12711] A stochastic matching model on hypergraphs

We study the stability of this stochastic system, for various hypergraph geometries. Subjects: Probability (math.PR) MSC classes: 60J10, 68M20, 05C65: Cite as: arXiv:1907.12711 [math.PR] (or arXiv:1907.12711v1 [math.PR] for this version) Submission history From: Pascal Moyal [v1] Tue, 30 Jul 2019 02:36:52 UTC (33 KB) Full-text links: ...

Stochastic Modeling and Analysis of Telecom Networks ...

Stochastic Modeling and Analysis of Telecom Networks - ISBN: 9781118563014 - (ebook) - von Laurent Decreusefond, Pascal Moyal, Verlag: Wiley

Moyal , Perry : On the instability of matching queues

Stability of the stochastic matching model Mairesse, Jean and Moyal, Pascal, Journal of Applied Probability, 2016; Stability of the bipartite matching model Bušić, Ana, Gupta, Varun, and Mairesse, Jean, Advances in Applied Probability, 2013; On the dynamic control of matching queues Gurvich, Itai and Ward, Amy, Stochastic Systems, 2014; Discrete-review policies for scheduling stochastic ...

STABILITY OF STOCHASTIC MATCHING SYSTEMS VIA FLUID LIMITS

STABILITY OF STOCHASTIC MATCHING SYSTEMS VIA FLUID LIMITS P. Moyal, Universit e de Technologie de Compi egne and Nothwestern University, pascal.moyal@utc.fr O. Perry, Northwestern University, ohad.perry@northwestern.edu Consider a model in which, to each node of a graph G is associated an arrival process. Upon arrival, any entering item associated to node k encounters the following alternative ...

Stochastic Modeling and Analysis of Telecoms Networks [Book]

by Laurent Decreusefond, Pascal Moyal. Released April 2012. Publisher(s): Wiley. ISBN: 9781848212381. Explore a preview version of Stochastic Modeling and Analysis of Telecoms Networks right now. O’Reilly members get unlimited access to live online training experiences, plus books, videos, and digital content from 200+ publishers. Start your free trial. Book Description. This book addresses ...

[0904.3240v1] Weak Solutions of stochastic recursions: an ...

Authors: Pascal Moyal (Submitted on 21 Apr 2009 (this version), latest version 7 Sep 2010 ( v2 )) Abstract: We propose an explicit construction of the solution of a stationary stochastic recursion of the form

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Pascal Moyal. Title: Stability of the stochastic matching problem. Abstract: (Based on common works with J. Mairesse and O. Perry) We study a stochastic discrete-event system in which items enter one by one, but which they leave by pairs. Possible pairs are decided by the so-called matching graph, which is fixed. The latter can be viewed as a generalization of skilled-based Customer/Server ...

Stochastic modeling and analysis of telecom networks (Book ...

Stochastic analysis 326 10.5. Problems 336 10.6. Notes and comments 337 Appendix A. Mathematical Toolbox 339 A.1. Probability spaces and processes 339 A.2. Conditional expectation 347 A.3. Vector spaces and orders 352 A.4. Bounded variation processes 356 A.5. Martingales 363 A.6. Laplace transform 378 A.7. Notes and comments 379 Bibliography 381 Index 385

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A generalized backwards scheme for solving non monotonic ...

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PROBABILITY - JSTOR

Moyal, Pascal, Tran, Viet Chi, Decreusefond, Laurent and Dhersin, Jean-Stéphane. Large graph limit for an SIR process in random network with heterogeneous connectivity 541-575 Muller, A., Radulescu, O., Crudu, A. and Debussche, A. Convergence of stochastic gene networks to hybrid piecewise deterministic processes Nagai, HIDEO. Downside risk ...

stochastic modeling and analysis of telecoms networks

Stochastic Modeling and Analysis of Telecom Networks www.it-ebooks.info Stochastic Modeling and Analysis of Telecom Networks Laurent Decreusefond Pascal Moyal Series. observation Stochastic Modeling and Analysis of Telecom Networks Laurent

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Archive ouverte HAL - Stability of the stochastic matching ...

Jean Mairesse, Pascal Moyal. Stability of the stochastic matching model. Journal of Applied Probability, Applied Probability Trust, 2016, 54 (3), pp.1064-1077. 10 ...

Construction of a stationary FIFO queue with impatient ...

In this paper, we study the stability of queues with impatient customers. Under general stationary ergodic assumptions, we first provide some conditions for...

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THE ANNALS of APPLIED PROBABILITY

pressure policies in stochastic processing networks. 2239-2299 Dayanik, Savas, Poor, H. Vincent and Sezer, Semih O. Multisource Bayesian sequential change detection. 552-590 Debussche, Arnaud and Gautier, Eric. Small noise asymp totic of the timing jitter in soliton transmission. 178-208 Decreusefond, Laurent and Moyal, Pascal. A functional

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On the stability of a class of non-monotonic systems of ...

Pascal Moyal 1 Discrete Event ... Decreusefond L, Moyal P (2012) Stochastic Modeling and Analysis of Telecom Networks. ISTE Wiley. Flipo D (1983) Steady State of Loss Systems. Comptes rendus de l’Acadmie des Sciences de Paris. Ser I 297(6) Flipo D (1989) Charge stationnaire d’une file d’attente à rejet. Cas de plusieurs serveurs. Annales scientifiques de l’universit de Clermont ...

Submitted to the Annals of Applied Probability

arXiv:1511.04282v1 [math.PR] 13 Nov 2015 Submitted to the Annals of Applied Probability ON THE INSTABILITY OF MATCHING QUEUES By Pascal Moyal∗ and Ohad Perry† Université de Technologie de ...

C1 - download.e-bookshelf.de

Firstpublished2012inGreatBritainandtheUnitedStatesbyISTELtdandJohnWiley&Sons,Inc. Apartfromanyfairdealingforthepurposesofresearchorprivatestudy,orcriticismorreview,as

Yuliya Mishura Theory and Statistical Applications of Stochastic Processes


This book is concerned with the theory of stochastic processes and the theoretical aspects of statistics for stochastic processes. It combines classic topics such as construction of stochastic processes, associated filtrations, processes with independent increments, Gaussian processes, martingales, Markov properties, continuity and related properties of trajectories with contemporary subjects: integration with respect to Gaussian processes, Itȏ integration, stochastic analysis, stochastic differential equations, fractional Brownian motion and parameter estimation in diffusion models.

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Mike Cheung W.-L. Meta-Analysis. A Structural Equation Modeling Approach


Presents a novel approach to conducting meta-analysis using structural equation modeling. Structural equation modeling (SEM) and meta-analysis are two powerful statistical methods in the educational, social, behavioral, and medical sciences. They are often treated as two unrelated topics in the literature. This book presents a unified framework on analyzing meta-analytic data within the SEM framework, and illustrates how to conduct meta-analysis using the metaSEM package in the R statistical environment. Meta-Analysis: A Structural Equation Modeling Approach begins by introducing the importance of SEM and meta-analysis in answering research questions. Key ideas in meta-analysis and SEM are briefly reviewed, and various meta-analytic models are then introduced and linked to the SEM framework. Fixed-, random-, and mixed-effects models in univariate and multivariate meta-analyses, three-level meta-analysis, and meta-analytic structural equation modeling, are introduced. Advanced topics, such as using restricted maximum likelihood estimation method and handling missing covariates, are also covered. Readers will learn a single framework to apply both meta-analysis and SEM. Examples in R and in Mplus are included. This book will be a valuable resource for statistical and academic researchers and graduate students carrying out meta-analyses, and will also be useful to researchers and statisticians using SEM in biostatistics. Basic knowledge of either SEM or meta-analysis will be helpful in understanding the materials in this book.

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Jian-Ming Jin Finite Element Analysis of Antennas and Arrays


The Most Complete, Up-to-Date Coverage of the Finite Element Analysis and Modeling of Antennas and Arrays Aimed at researchers as well as practical engineers—and packed with over 200 illustrations including twenty-two color plates—Finite Element Analysis of Antennas and Arrays presents: Time- and frequency-domain formulations and mesh truncation techniques Antenna source modeling and parameter calculation Modeling of complex materials and fine geometrical details Analysis and modeling of narrowband and broadband antennas Analysis and modeling of infinite and finite phased-array antennas Analysis and modeling of antenna and platform interactions Recognizing the strengths of other numerical methods, this book goes beyond the finite element method and covers hybrid techniques that combine the finite element method with the finite difference time-domain method, the method of moments, and the high-frequency asymptotic methods to efficiently deal with a variety of complex antenna problems. Complemented with numerous examples, this cutting-edge resource fully demonstrates the power and capabilities of the finite element analysis and its many practical applications.

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Michael J. Panik Stochastic Differential Equations


A beginner’s guide to stochastic growth modeling The chief advantage of stochastic growth models over deterministic models is that they combine both deterministic and stochastic elements of dynamic behaviors, such as weather, natural disasters, market fluctuations, and epidemics. This makes stochastic modeling a powerful tool in the hands of practitioners in fields for which population growth is a critical determinant of outcomes. However, the background requirements for studying SDEs can be daunting for those who lack the rigorous course of study received by math majors. Designed to be accessible to readers who have had only a few courses in calculus and statistics, this book offers a comprehensive review of the mathematical essentials needed to understand and apply stochastic growth models. In addition, the book describes deterministic and stochastic applications of population growth models including logistic, generalized logistic, Gompertz, negative exponential, and linear. Ideal for students and professionals in an array of fields including economics, population studies, environmental sciences, epidemiology, engineering, finance, and the biological sciences, Stochastic Differential Equations: An Introduction with Applications in Population Dynamics Modeling: • Provides precise definitions of many important terms and concepts and provides many solved example problems • Highlights the interpretation of results and does not rely on a theorem-proof approach • Features comprehensive chapters addressing any background deficiencies readers may have and offers a comprehensive review for those who need a mathematics refresher • Emphasizes solution techniques for SDEs and their practical application to the development of stochastic population models An indispensable resource for students and practitioners with limited exposure to mathematics and statistics, Stochastic Differential Equations: An Introduction with Applications in Population Dynamics Modeling is an excellent fit for advanced undergraduates and beginning graduate students, as well as practitioners who need a gentle introduction to SDEs. Michael J. Panik, PhD, is Professor in the Department of Economics, Barney School of Business and Public Administration at the University of Hartford in Connecticut. He received his PhD in Economics from Boston College and is a member of the American Mathematical Society, The American Statistical Association, and The Econometric Society.

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Группа авторов Flowgraph Models for Multistate Time-to-Event Data


A unique introduction to the innovative methodology of statistical flowgraphs This book offers a practical, application-based approach to flowgraph models for time-to-event data. It clearly shows how this innovative new methodology can be used to analyze data from semi-Markov processes without prior knowledge of stochastic processes–opening the door to interesting applications in survival analysis and reliability as well as stochastic processes. Unlike other books on multistate time-to-event data, this work emphasizes reliability and not just biostatistics, illustrating each method with medical and engineering examples. It demonstrates how flowgraphs bring together applied probability techniques and combine them with data analysis and statistical methods to answer questions of practical interest. Bayesian methods of data analysis are emphasized. Coverage includes: * Clear instructions on how to model multistate time-to-event data using flowgraph models * An emphasis on computation, real data, and Bayesian methods for problem solving * Real-world examples for analyzing data from stochastic processes * The use of flowgraph models to analyze complex stochastic networks * Exercise sets to reinforce the practical approach of this volume Flowgraph Models for Multistate Time-to-Event Data is an invaluable resource/reference for researchers in biostatistics/survival analysis, systems engineering, and in fields that use stochastic processes, including anthropology, biology, psychology, computer science, and engineering.

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Saleh Joseph Homer Spacecraft Reliability and Multi-State Failures. A Statistical Approach


The aerospace community has long recognized and repeatedly emphasizes the importance of reliability for space systems. Despite this, little has been published in book form on the topic. Spacecraft Reliability and Multi-state Failures addresses this gap in the literature, offering a unique focus on spacecraft reliability based on extensive statistical analysis of system and subsystem anomalies and failures. The authors provide new results pertaining to spacecraft reliability based on extensive statistical analysis of on-orbit anomaly and failure data that will be particularly useful to spacecraft manufacturers and designers, for example in guiding satellite (and subsystem) test and screening programs and providing an empirical basis for subsystem redundancy and reliability growth plans. The authors develop nonparametric results and parametric models of spacecraft and spacecraft subsystem reliability and multi-state failures, quantify the relative contribution of each subsystem to the failure of the satellites thus identifying the subsystems that drive spacecraft unreliability, and propose advanced stochastic modeling and analysis tools for the reliability and survivability of spacecraft and space-based networks. Spacecraft Reliability and Multi-state Failures provides new nonparametric results pertaining to spacecraft reliability based on extensive statistical analysis of on-orbit anomaly and failure data; develops parametric models of spacecraft and spacecraft subsystem reliability and multi-state failures quantifies the relative contribution of each subsystem to the failure of the satellites proposes advanced stochastic modeling and analysis tools for the reliability and survivability of spacecraft and space-based networks. provides a dedicated treatment of the reliability and subsystem anomalies of communication spacecraft in geostationary orbit.

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Cho W. S. To Stochastic Structural Dynamics. Application of Finite Element Methods


One of the first books to provide in-depth and systematic application of finite element methods to the field of stochastic structural dynamics The parallel developments of the Finite Element Methods in the 1950’s and the engineering applications of stochastic processes in the 1940’s provided a combined numerical analysis tool for the studies of dynamics of structures and structural systems under random loadings. In the open literature, there are books on statistical dynamics of structures and books on structural dynamics with chapters dealing with random response analysis. However, a systematic treatment of stochastic structural dynamics applying the finite element methods seems to be lacking. Aimed at advanced and specialist levels, the author presents and illustrates analytical and direct integration methods for analyzing the statistics of the response of structures to stochastic loads. The analysis methods are based on structural models represented via the Finite Element Method. In addition to linear problems the text also addresses nonlinear problems and non-stationary random excitation with systems having large spatially stochastic property variations. A systematic treatment of stochastic structural dynamics applying the finite element methods Highly illustrated throughout and aimed at advanced and specialist levels, it focuses on computational aspects instead of theory Emphasizes results mainly in the time domain with limited contents in the time-frequency domain Presents and illustrates direction integration methods for analyzing the statistics of the response of linear and nonlinear structures to stochastic loads Under Author Information – one change of word to existing text: He is a Fellow of the American Society of Mechanical Engineers (ASME)........

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Jichuan Wang Structural Equation Modeling


Presents a useful guide for applications of SEM whilst systematically demonstrating various SEM models using M plus Focusing on the conceptual and practical aspects of Structural Equation Modeling (SEM), this book demonstrates basic concepts and examples of various SEM models, along with updates on many advanced methods, including confirmatory factor analysis (CFA) with categorical items, bifactor model, Bayesian CFA model, item response theory (IRT) model, graded response model (GRM), multiple imputation (MI) of missing values, plausible values of latent variables, moderated mediation model, Bayesian SEM, latent growth modeling (LGM) with individually varying times of observations, dynamic structural equation modeling (DSEM), residual dynamic structural equation modeling (RDSEM), testing measurement invariance of instrument with categorical variables, longitudinal latent class analysis (LLCA), latent transition analysis (LTA), growth mixture modeling (GMM) with covariates and distal outcome, manual implementation of the BCH method and the three-step method for mixture modeling, Monte Carlo simulation power analysis for various SEM models, and estimate sample size for latent class analysis (LCA) model. The statistical modeling program Mplus Version 8. 2 is featured with all models updated. It provides researchers with a flexible tool that allows them to analyze data with an easy-to-use interface and graphical displays of data and analysis results. Intended as both a teaching resource and a reference guide, and written in non-mathematical terms, Structural Equation Modeling: Applications Using Mplus, 2nd edition provides step-by-step instructions of model specification, estimation, evaluation, and modification. Chapters cover: Confirmatory Factor Analysis (CFA); Structural Equation Models (SEM); SEM for Longitudinal Data; Multi-Group Models; Mixture Models; and Power Analysis and Sample Size Estimate for SEM. Presents a useful reference guide for applications of SEM while systematically demonstrating various advanced SEM models Discusses and demonstrates various SEM models using both cross-sectional and longitudinal data with both continuous and categorical outcomes Provides step-by-step instructions of model specification and estimation, as well as detailed interpretation of M plus results using real data sets Introduces different methods for sample size estimate and statistical power analysis for SEM Structural Equation Modeling is an excellent book for researchers and graduate students of SEM who want to understand the theory and learn how to build their own SEM models using M plus.

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Группа авторов Handbook of High-Frequency Trading and Modeling in Finance


Reflecting the fast pace and ever-evolving nature of the financial industry, the Handbook of High-Frequency Trading and Modeling in Finance details how high-frequency analysis presents new systematic approaches to implementing quantitative activities with high-frequency financial data. Introducing new and established mathematical foundations necessary to analyze realistic market models and scenarios, the handbook begins with a presentation of the dynamics and complexity of futures and derivatives markets as well as a portfolio optimization problem using quantum computers. Subsequently, the handbook addresses estimating complex model parameters using high-frequency data. Finally, the handbook focuses on the links between models used in financial markets and models used in other research areas such as geophysics, fossil records, and earthquake studies. The Handbook of High-Frequency Trading and Modeling in Finance also features: • Contributions by well-known experts within the academic, industrial, and regulatory fields • A well-structured outline on the various data analysis methodologies used to identify new trading opportunities • Newly emerging quantitative tools that address growing concerns relating to high-frequency data such as stochastic volatility and volatility tracking; stochastic jump processes for limit-order books and broader market indicators; and options markets • Practical applications using real-world data to help readers better understand the presented material The Handbook of High-Frequency Trading and Modeling in Finance is an excellent reference for professionals in the fields of business, applied statistics, econometrics, and financial engineering. The handbook is also a good supplement for graduate and MBA-level courses on quantitative finance, volatility, and financial econometrics. Ionut Florescu, PhD, is Research Associate Professor in Financial Engineering and Director of the Hanlon Financial Systems Laboratory at Stevens Institute of Technology. His research interests include stochastic volatility, stochastic partial differential equations, Monte Carlo Methods, and numerical methods for stochastic processes. Dr. Florescu is the author of Probability and Stochastic Processes, the coauthor of Handbook of Probability, and the coeditor of Handbook of Modeling High-Frequency Data in Finance, all published by Wiley. Maria C. Mariani, PhD, is Shigeko K. Chan Distinguished Professor in Mathematical Sciences and Chair of the Department of Mathematical Sciences at The University of Texas at El Paso. Her research interests include mathematical finance, applied mathematics, geophysics, nonlinear and stochastic partial differential equations and numerical methods. Dr. Mariani is the coeditor of Handbook of Modeling High-Frequency Data in Finance, also published by Wiley. H. Eugene Stanley, PhD, is William Fairfield Warren Distinguished Professor at Boston University. Stanley is one of the key founders of the new interdisciplinary field of econophysics, and has an ISI Hirsch index H=128 based on more than 1200 papers. In 2004 he was elected to the National Academy of Sciences. Frederi G. Viens, PhD, is Professor of Statistics and Mathematics and Director of the Computational Finance Program at Purdue University. He holds more than two dozen local, regional, and national awards and he travels extensively on a world-wide basis to deliver lectures on his research interests, which range from quantitative finance to climate science and agricultural economics. A Fellow of the Institute of Mathematics Statistics, Dr. Viens is the coeditor of Handbook of Modeling High-Frequency Data in Finance, also published by Wiley.

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Jie Li Stochastic Dynamics of Structures


In Stochastic Dynamics of Structures, Li and Chen present a unified view of the theory and techniques for stochastic dynamics analysis, prediction of reliability, and system control of structures within the innovative theoretical framework of physical stochastic systems. The authors outline the fundamental concepts of random variables, stochastic process and random field, and orthogonal expansion of random functions. Readers will gain insight into core concepts such as stochastic process models for typical dynamic excitations of structures, stochastic finite element, and random vibration analysis. Li and Chen also cover advanced topics, including the theory of and elaborate numerical methods for probability density evolution analysis of stochastic dynamical systems, reliability-based design, and performance control of structures. Stochastic Dynamics of Structures presents techniques for researchers and graduate students in a wide variety of engineering fields: civil engineering, mechanical engineering, aerospace and aeronautics, marine and offshore engineering, ship engineering, and applied mechanics. Practicing engineers will benefit from the concise review of random vibration theory and the new methods introduced in the later chapters. «The book is a valuable contribution to the continuing development of the field of stochastic structural dynamics, including the recent discoveries and developments by the authors of the probability density evolution method (PDEM) and its applications to the assessment of the dynamic reliability and control of complex structures through the equivalent extreme-value distribution.» —A. H-S. Ang, NAE, Hon. Mem. ASCE, Research Professor, University of California, Irvine, USA «The authors have made a concerted effort to present a responsible and even holistic account of modern stochastic dynamics. Beyond the traditional concepts, they also discuss theoretical tools of recent currency such as the Karhunen-Loeve expansion, evolutionary power spectra, etc. The theoretical developments are properly supplemented by examples from earthquake, wind, and ocean engineering. The book is integrated by also comprising several useful appendices, and an exhaustive list of references; it will be an indispensable tool for students, researchers, and practitioners endeavoring in its thematic field.» —Pol Spanos, NAE, Ryon Chair in Engineering, Rice University, Houston, USA

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