Modeling Random Systems
Modeling Random Systems

Author: J. R. Cogdell

Publisher: Pearson Prentice Hall

Published: 2004

Total Pages: 728

ISBN-13:

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For undergraduate courses in probability, statistics, and random processes in Engineering, especially Electrical Engineering. This text equips students in engineering and other technical areas to understand, analyze, and design systems that have random aspects. Material on probability, statistics, and random processes is presented in a style that appeals to engineering interests and avoids excessive mathematical development. The unifying concept throughout the book is "modeling": probability is defined as a model for data, expectations model averages, the various distributions model real-world situations, random processes model analog and digital information-bearing signals, and white noise models wideband noise from physical processes.

Random Processes for Engineers
Random Processes for Engineers

Author: Bruce Hajek

Publisher: Cambridge University Press

Published: 2015-03-12

Total Pages: 429

ISBN-13: 1316241246

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This engaging introduction to random processes provides students with the critical tools needed to design and evaluate engineering systems that must operate reliably in uncertain environments. A brief review of probability theory and real analysis of deterministic functions sets the stage for understanding random processes, whilst the underlying measure theoretic notions are explained in an intuitive, straightforward style. Students will learn to manage the complexity of randomness through the use of simple classes of random processes, statistical means and correlations, asymptotic analysis, sampling, and effective algorithms. Key topics covered include: • Calculus of random processes in linear systems • Kalman and Wiener filtering • Hidden Markov models for statistical inference • The estimation maximization (EM) algorithm • An introduction to martingales and concentration inequalities. Understanding of the key concepts is reinforced through over 100 worked examples and 300 thoroughly tested homework problems (half of which are solved in detail at the end of the book).

Random Growth Models
Random Growth Models

Author: Michael Damron

Publisher: American Mathematical Soc.

Published: 2018-09-27

Total Pages: 274

ISBN-13: 1470435535

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The study of random growth models began in probability theory about 50 years ago, and today this area occupies a central place in the subject. The considerable challenges posed by these models have spurred the development of innovative probability theory and opened up connections with several other parts of mathematics, such as partial differential equations, integrable systems, and combinatorics. These models also have applications to fields such as computer science, biology, and physics. This volume is based on lectures delivered at the 2017 AMS Short Course “Random Growth Models”, held January 2–3, 2017 in Atlanta, GA. The articles in this book give an introduction to the most-studied models; namely, first- and last-passage percolation, the Eden model of cell growth, and particle systems, focusing on the main research questions and leading up to the celebrated Kardar-Parisi-Zhang equation. Topics covered include asymptotic properties of infection times, limiting shape results, fluctuation bounds, and geometrical properties of geodesics, which are optimal paths for growth.

An Introduction to Stochastic Modeling
An Introduction to Stochastic Modeling

Author: Howard M. Taylor

Publisher: Academic Press

Published: 2014-05-10

Total Pages: 410

ISBN-13: 1483269272

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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.

Probability
Probability

Author: Gregory K. Miller

Publisher: Wiley-Interscience

Published: 2006-08-25

Total Pages: 496

ISBN-13:

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Improve Your Probability of Mastering This Topic This book takes an innovative approach to calculus-based probability theory, considering it within a framework for creating models of random phenomena. The author focuses on the synthesis of stochastic models concurrent with the development of distribution theory while also introducing the reader to basic statistical inference. In this way, the major stochastic processes are blended with coverage of probability laws, random variables, and distribution theory, equipping the reader to be a true problem solver and critical thinker. Deliberately conversational in tone, Probability is written for students in junior- or senior-level probability courses majoring in mathematics, statistics, computer science, or engineering. The book offers a lucid and mathematicallysound introduction to how probability is used to model random behavior in the natural world. The text contains the following chapters: Modeling Sets and Functions Probability Laws I: Building on the Axioms Probability Laws II: Results of Conditioning Random Variables and Stochastic Processes Discrete Random Variables and Applications in Stochastic Processes Continuous Random Variables and Applications in Stochastic Processes Covariance and Correlation Among Random Variables Included exercises cover a wealth of additional concepts, such as conditional independence, Simpson's paradox, acceptance sampling, geometric probability, simulation, exponential families of distributions, Jensen's inequality, and many non-standard probability distributions.

Modeling Random Processes for Engineers and Managers
Modeling Random Processes for Engineers and Managers

Author: James J. Solberg

Publisher: John Wiley & Sons

Published: 2008-12-22

Total Pages: 320

ISBN-13: 0470322551

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Modeling Random Processes for Engineers and Managers provides students with a "gentle" introduction to stochastic processes, emphasizing full explanations and many examples rather than formal mathematical theorems and proofs. The text offers an accessible entry into a very useful and versatile set of tools for dealing with uncertainty and variation. Many practical examples of models, as well as complete explanations of the thought process required to create them, motivate the presentation of the computational methods. In addition, the text contains a previously unpublished computational approach to solving many of the equations that occur in Markov processes. Modeling Random Processes is intended to serve as an introduction, but more advanced students can use the case studies and problems to expand their understanding of practical uses of the theory.

Applied Mathematical Analysis: Theory, Methods, and Applications
Applied Mathematical Analysis: Theory, Methods, and Applications

Author: Hemen Dutta

Publisher: Springer

Published: 2019-02-21

Total Pages: 809

ISBN-13: 3319999184

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This book addresses key aspects of recent developments in applied mathematical analysis and its use. It also highlights a broad range of applications from science, engineering, technology and social perspectives. Each chapter investigates selected research problems and presents a balanced mix of theory, methods and applications for the chosen topics. Special emphasis is placed on presenting basic developments in applied mathematical analysis, and on highlighting the latest advances in this research area. The book is presented in a self-contained manner as far as possible, and includes sufficient references to allow the interested reader to pursue further research in this still-developing field. The primary audience for this book includes graduate students, researchers and educators; however, it will also be useful for general readers with an interest in recent developments in applied mathematical analysis and applications.

Modeling with Itô Stochastic Differential Equations
Modeling with Itô Stochastic Differential Equations

Author: E. Allen

Publisher: Springer Science & Business Media

Published: 2007-03-08

Total Pages: 239

ISBN-13: 1402059531

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This book explains a procedure for constructing realistic stochastic differential equation models for randomly varying systems in biology, chemistry, physics, engineering, and finance. Introductory chapters present the fundamental concepts of random variables, stochastic processes, stochastic integration, and stochastic differential equations. These concepts are explained in a Hilbert space setting which unifies and simplifies the presentation.

Mathematical Modeling of Random and Deterministic Phenomena
Mathematical Modeling of Random and Deterministic Phenomena

Author: Solym Mawaki Manou-Abi

Publisher: John Wiley & Sons

Published: 2020-04-28

Total Pages: 308

ISBN-13: 1786304546

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This book highlights mathematical research interests that appear in real life, such as the study and modeling of random and deterministic phenomena. As such, it provides current research in mathematics, with applications in biological and environmental sciences, ecology, epidemiology and social perspectives. The chapters can be read independently of each other, with dedicated references specific to each chapter. The book is organized in two main parts. The first is devoted to some advanced mathematical problems regarding epidemic models; predictions of biomass; space-time modeling of extreme rainfall; modeling with the piecewise deterministic Markov process; optimal control problems; evolution equations in a periodic environment; and the analysis of the heat equation. The second is devoted to a modelization with interdisciplinarity in ecological, socio-economic, epistemological, demographic and social problems. Mathematical Modeling of Random and Deterministic Phenomena is aimed at expert readers, young researchers, plus graduate and advanced undergraduate students who are interested in probability, statistics, modeling and mathematical analysis.

The Random-Cluster Model
The Random-Cluster Model

Author: Geoffrey R. Grimmett

Publisher: Springer Science & Business Media

Published: 2006-12-13

Total Pages: 392

ISBN-13: 3540328912

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The random-cluster model has emerged as a key tool in the mathematical study of ferromagnetism. It may be viewed as an extension of percolation to include Ising and Potts models, and its analysis is a mix of arguments from probability and geometry. The Random-Cluster Model contains accounts of the subcritical and supercritical phases, together with clear statements of important open problems. The book includes treatment of the first-order (discontinuous) phase transition.