- Filename: probability-and-stochastic-modeling.
- ISBN: 9781439872062
- Release Date: 2012-08-25
- Number of pages: 508
- Author: Vladimir I. Rotar
- Publisher: CRC Press

A First Course in Probability with an Emphasis on Stochastic Modeling Probability and Stochastic Modeling not only covers all the topics found in a traditional introductory probability course, but also emphasizes stochastic modeling, including Markov chains, birth-death processes, and reliability models. Unlike most undergraduate-level probability texts, the book also focuses on increasingly important areas, such as martingales, classification of dependency structures, and risk evaluation. Numerous examples, exercises, and models using real-world data demonstrate the practical possibilities and restrictions of different approaches and help students grasp general concepts and theoretical results. The text is suitable for majors in mathematics and statistics as well as majors in computer science, economics, finance, and physics. The author offers two explicit options to teaching the material, which is reflected in "routes" designated by special "roadside" markers. The first route contains basic, self-contained material for a one-semester course. The second provides a more complete exposition for a two-semester course or self-study.

- Filename: concepts-in-probability-and-stochastic-modeling.
- ISBN: UOM:39015033980346
- Release Date: 1995
- Number of pages: 420
- Author: James J. Higgins
- Publisher: Duxbury Resource Center

This text stresses modern ideas, including simulation and interpretation of results. It focuses on the aspects of probability most relevant to applications, such as stochastic modeling, Markov chains, reliability, and queuing.

- Filename: probability-and-stochastic-modeling.
- ISBN: 9781497025424
- Release Date: 2016-10-17
- Number of pages: 34
- Author: CTI Reviews
- Publisher: Cram101 Textbook Reviews

Facts101 is your complete guide to Probability and Stochastic Modeling. In this book, you will learn topics such as as those in your book plus much more. With key features such as key terms, people and places, Facts101 gives you all the information you need to prepare for your next exam. Our practice tests are specific to the textbook and we have designed tools to make the most of your limited study time.

- Filename: an-introduction-to-stochastic-modeling.
- ISBN: 9780123814166
- Release Date: 2011
- Number of pages: 563
- Author: Mark A. Pinsky
- Publisher: Academic Press

Serving as the foundation for a one-semester course in stochastic processes for students familiar with elementary probability theory and calculus, Introduction to Stochastic Modeling, Third Edition, bridges the gap between basic probability and an intermediate level course in stochastic processes. The objectives of the text are to introduce students to the standard concepts and methods of stochastic modeling, to illustrate the rich diversity of applications of stochastic processes in the applied sciences, and to provide exercises in the application of simple stochastic analysis to realistic problems. * Realistic applications from a variety of disciplines integrated throughout the text * Plentiful, updated and more rigorous problems, including computer "challenges" * Revised end-of-chapter exercises sets-in all, 250 exercises with answers * New chapter on Brownian motion and related processes * Additional sections on Matingales and Poisson process * Solutions manual available to adopting instructors

- Filename: stochastic-modeling.
- ISBN: 9780486139944
- Release Date: 2012-10-11
- Number of pages: 336
- Author: Barry L. Nelson
- Publisher: Courier Corporation

Coherent introduction to techniques also offers a guide to the mathematical, numerical, and simulation tools of systems analysis. Includes formulation of models, analysis, and interpretation of results. 1995 edition.

- Filename: introduction-to-matrix-analytic-methods-in-stochastic-modeling.
- ISBN: 0898719739
- Release Date: 1999
- Number of pages: 334
- Author: G. Latouche
- Publisher: SIAM

Matrix analytic methods are popular as modeling tools because they give one the ability to construct and analyze a wide class of queuing models in a unified and algorithmically tractable way. The authors present the basic mathematical ideas and algorithms of the matrix analytic theory in a readable, up-to-date, and comprehensive manner. In the current literature, a mixed bag of techniques is used-some probabilistic, some from linear algebra, and some from transform methods. Here, many new proofs that emphasize the unity of the matrix analytic approach are included.

- Filename: probability-stochastic-processes-and-queueing-theory.
- ISBN: 0387944524
- Release Date: 1995-06-13
- Number of pages: 583
- Author: Randolph Nelson
- Publisher: Springer Science & Business Media

We will occasionally footnote a portion of text with a "**,, to indicate Notes on the that this portion can be initially bypassed. The reasons for bypassing a Text portion of the text include: the subject is a special topic that will not be referenced later, the material can be skipped on first reading, or the level of mathematics is higher than the rest of the text. In cases where a topic is self-contained, we opt to collect the material into an appendix that can be read by students at their leisure. The material in the text cannot be fully assimilated until one makes it Notes on "their own" by applying the material to specific problems. Self-discovery Problems is the best teacher and although they are no substitute for an inquiring mind, problems that explore the subject from different viewpoints can often help the student to think about the material in a uniquely per sonal way. With this in mind, we have made problems an integral part of this work and have attempted to make them interesting as well as informative.

- Filename: stochastic-modelling-of-electricity-and-related-markets.
- ISBN: 9789812812315
- Release Date: 2008
- Number of pages: 337
- Author: Fred Espen Benth
- Publisher: World Scientific

The markets for electricity, gas and temperature have distinctive features, which provide the focus for countless studies. For instance, electricity and gas prices may soar several magnitudes above their normal levels within a short time due to imbalances in supply and demand, yielding what is known as spikes in the spot prices. The markets are also largely influenced by seasons, since power demand for heating and cooling varies over the year. The incompleteness of the markets, due to nonstorability of electricity and temperature as well as limited storage capacity of gas, makes spot-forward hedging impossible. Moreover, futures contracts are typically settled over a time period rather than at a fixed date. All these aspects of the markets create new challenges when analyzing price dynamics of spot, futures and other derivatives. This book provides a concise and rigorous treatment on the stochastic modeling of energy markets. OrnsteinOCoUhlenbeck processes are described as the basic modeling tool for spot price dynamics, where innovations are driven by time-inhomogeneous jump processes. Temperature futures are studied based on a continuous higher-order autoregressive model for the temperature dynamics. The theory presented here pays special attention to the seasonality of volatility and the Samuelson effect. Empirical studies using data from electricity, temperature and gas markets are given to link theory to practice. Sample Chapter(s). A Survey of Electricity and Related Markets (331 KB). Contents: A Survey of Electricity and Related Markets; Stochastic Analysis for Independent Increment Processes; Stochastic Models for the Energy Spot Price Dynamics; Pricing of Forwards and Swaps Based on the Spot Price; Applications to the Gas Markets; Modeling Forwards and Swaps Using the HeathOCoJarrowOCoMorton Approach; Constructing Smooth Forward Curves in Electricity Markets; Modeling of the Electricity Futures Market; Pricing and Hedging of Energy Options; Analysis of Temperature Derivatives. Readership: Researchers in energy and commodity markets, and mathematical finance.

- Filename: probability-stochastic-processes-and-queueing-theory.
- ISBN: 9781475724264
- Release Date: 2013-06-29
- Number of pages: 584
- Author: Randolph Nelson
- Publisher: Springer Science & Business Media

We will occasionally footnote a portion of text with a "**,, to indicate Notes on the that this portion can be initially bypassed. The reasons for bypassing a Text portion of the text include: the subject is a special topic that will not be referenced later, the material can be skipped on first reading, or the level of mathematics is higher than the rest of the text. In cases where a topic is self-contained, we opt to collect the material into an appendix that can be read by students at their leisure. The material in the text cannot be fully assimilated until one makes it Notes on "their own" by applying the material to specific problems. Self-discovery Problems is the best teacher and although they are no substitute for an inquiring mind, problems that explore the subject from different viewpoints can often help the student to think about the material in a uniquely per sonal way. With this in mind, we have made problems an integral part of this work and have attempted to make them interesting as well as informative.

- Filename: stochastic-modeling-in-broadband-communications-systems.
- ISBN: 9780898715194
- Release Date: 2002-01
- Number of pages: 177
- Author: Ingemar Kaj
- Publisher: SIAM

A concise overview of stochastic models and mathematical techniques for solving problems that arise in broadband communication systems.

- Filename: introduction-to-probability-models.
- ISBN: 0123756871
- Release Date: 2006-12-11
- Number of pages: 800
- Author: Sheldon M. Ross
- Publisher: Academic Press

Introduction to Probability Models, Tenth Edition, provides an introduction to elementary probability theory and stochastic processes. There are two approaches to the study of probability theory. One is heuristic and nonrigorous, and attempts to develop in students an intuitive feel for the subject that enables him or her to think probabilistically. The other approach attempts a rigorous development of probability by using the tools of measure theory. The first approach is employed in this text. The book begins by introducing basic concepts of probability theory, such as the random variable, conditional probability, and conditional expectation. This is followed by discussions of stochastic processes, including Markov chains and Poison processes. The remaining chapters cover queuing, reliability theory, Brownian motion, and simulation. Many examples are worked out throughout the text, along with exercises to be solved by students. This book will be particularly useful to those interested in learning how probability theory can be applied to the study of phenomena in fields such as engineering, computer science, management science, the physical and social sciences, and operations research. Ideally, this text would be used in a one-year course in probability models, or a one-semester course in introductory probability theory or a course in elementary stochastic processes. New to this Edition: 65% new chapter material including coverage of finite capacity queues, insurance risk models and Markov chains Contains compulsory material for new Exam 3 of the Society of Actuaries containing several sections in the new exams Updated data, and a list of commonly used notations and equations, a robust ancillary package, including a ISM, SSM, test bank, and companion website Includes SPSS PASW Modeler and SAS JMP software packages which are widely used in the field Hallmark features: Superior writing style Excellent exercises and examples covering the wide breadth of coverage of probability topics Real-world applications in engineering, science, business and economics

- Filename: a-first-course-in-stochastic-models.
- ISBN: 0471498807
- Release Date: 2003-04-18
- Number of pages: 478
- Author: H. C. Tijms
- Publisher: John Wiley & Sons

An integrated presentation of theory, applications and algorithms that demonstrates how useful simple stochastic models can be for gaining insight into the behaviour of complex stochastic systems.

- Filename: probability-models.
- ISBN: 1852334312
- Release Date: 2002-01-01
- Number of pages: 256
- Author: John Haigh
- Publisher: Springer Science & Business Media

An introduction to probability for undergraduate students, this book draws on everyday experience (games with dice; weather patterns, betting on sports events) and includes a wide range of problems and exercises, from the routine to the more challenging, for self-study.

- Filename: stochastic-modeling-of-microstructures.
- ISBN: 9781461201212
- Release Date: 2012-12-06
- Number of pages: 270
- Author: Kazimierz Sobczyk
- Publisher: Springer Science & Business Media

This book is for a general scientific and engineering audience as a guide to current ideas, methods, and models for stochastic modeling of microstructures. It is a reference for professionals in material modeling, mechanical engineering, materials science, chemical, civil, environmental engineering and applied mathematics.

- Filename: stochastic-modeling-in-economics-and-finance.
- ISBN: 9781402008405
- Release Date: 2002-08-31
- Number of pages: 386
- Author: Jitka Dupa?ovĂˇ
- Publisher: Springer Science & Business Media

Unlike other books that focus only on selected specific subjects this book provides both a broad and rich cross-section of contemporary approaches to stochastic modeling in finance and economics; it is decision making oriented. The material ranges from common tools to solutions of sophisticated system problems and applications. In Part I, the fundamentals of financial thinking and elementary mathematical methods of finance are presented. The method of presentation is simple enough to bridge the elements of financial arithmetic and complex models of financial math developed in the later parts. It covers characteristics of cash flows, yield curves, and valuation of securities. Part II is devoted to the allocation of funds and risk management: classics (Markowitz theory of portfolio), capital asset pricing model, arbitrage pricing theory, asset & liability management, value at risk. The method explanation takes into account the computational aspects. Part III explains modeling aspects of multistage stochastic programming on a relatively accessible level. It includes a survey of existing software, links to parametric, multiobjective and dynamic programming, and to probability and statistics. It focuses on scenario-based problems with the problems of scenario generation and output analysis discussed in detail and illustrated within a case study. Selected examples of successful applications in finance, production planning and management of technological processes and electricity generation are presented. Throughout, the emphasis is on the appropriate use of the techniques, rather than on the underlying mathematical proofs and theories. In Part IV, the sections devoted to stochastic calculus cover also more advanced topics such as DDS Theorem or extremal martingale measures, which make it possible to treat more delicate models in Mathematical Finance (complete markets, optimal control, etc.) Audience: Students and researchers in probability and statistics, econometrics, operations research and various fields of finance, economics, engineering, and insurance.