# Stochastic Modelling in Process Technology

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## Stochastic Modelling in Process Technology

Author | : Herold G. Dehling,Timo Gottschalk,Alex C. Hoffmann |

Publsiher | : Elsevier |

Total Pages | : 290 |

Release | : 2007-07-03 |

ISBN 10 | : 9780080548975 |

ISBN 13 | : 0080548970 |

Language | : EN, FR, DE, ES & NL |

**Stochastic Modelling in Process Technology Book Review:**

There is an ever increasing need for modelling complex processes reliably. Computational modelling techniques, such as CFD and MD may be used as tools to study specific systems, but their emergence has not decreased the need for generic, analytical process models. Multiphase and multicomponent systems, and high-intensity processes displaying a highly complex behaviour are becoming omnipresent in the processing industry. This book discusses an elegant, but little-known technique for formulating process models in process technology: stochastic process modelling. The technique is based on computing the probability distribution for a single particle's position in the process vessel, and/or the particle's properties, as a function of time, rather than - as is traditionally done - basing the model on the formulation and solution of differential conservation equations. Using this technique can greatly simplify the formulation of a model, and even make modelling possible for processes so complex that the traditional method is impracticable. Stochastic modelling has sporadically been used in various branches of process technology under various names and guises. This book gives, as the first, an overview of this work, and shows how these techniques are similar in nature, and make use of the same basic mathematical tools and techniques. The book also demonstrates how stochastic modelling may be implemented by describing example cases, and shows how a stochastic model may be formulated for a case, which cannot be described by formulating and solving differential balance equations. Introduction to stochastic process modelling as an alternative modelling technique Shows how stochastic modelling may be succesful where the traditional technique fails Overview of stochastic modelling in process technology in the research literature Illustration of the principle by a wide range of practical examples In-depth and self-contained discussions Points the way to both mathematical and technological research in a new, rewarding field

## Markov Processes for Stochastic Modeling

Author | : Oliver Ibe |

Publsiher | : Newnes |

Total Pages | : 514 |

Release | : 2013-05-22 |

ISBN 10 | : 0124078397 |

ISBN 13 | : 9780124078390 |

Language | : EN, FR, DE, ES & NL |

**Markov Processes for Stochastic Modeling Book Review:**

Markov processes are processes that have limited memory. In particular, their dependence on the past is only through the previous state. They are used to model the behavior of many systems including communications systems, transportation networks, image segmentation and analysis, biological systems and DNA sequence analysis, random atomic motion and diffusion in physics, social mobility, population studies, epidemiology, animal and insect migration, queueing systems, resource management, dams, financial engineering, actuarial science, and decision systems. Covering a wide range of areas of application of Markov processes, this second edition is revised to highlight the most important aspects as well as the most recent trends and applications of Markov processes. The author spent over 16 years in the industry before returning to academia, and he has applied many of the principles covered in this book in multiple research projects. Therefore, this is an applications-oriented book that also includes enough theory to provide a solid ground in the subject for the reader. Presents both the theory and applications of the different aspects of Markov processes Includes numerous solved examples as well as detailed diagrams that make it easier to understand the principle being presented Discusses different applications of hidden Markov models, such as DNA sequence analysis and speech analysis.

## GPS Stochastic Modelling

Author | : Xiaoguang Luo |

Publsiher | : Springer Science & Business Media |

Total Pages | : 331 |

Release | : 2013-07-06 |

ISBN 10 | : 364234836X |

ISBN 13 | : 9783642348365 |

Language | : EN, FR, DE, ES & NL |

**GPS Stochastic Modelling Book Review:**

Global Navigation Satellite Systems (GNSS), such as GPS, have become an efficient, reliable and standard tool for a wide range of applications. However, when processing GNSS data, the stochastic model characterising the precision of observations and the correlations between them is usually simplified and incomplete, leading to overly optimistic accuracy estimates. This work extends the stochastic model using signal-to-noise ratio (SNR) measurements and time series analysis of observation residuals. The proposed SNR-based observation weighting model significantly improves the results of GPS data analysis, while the temporal correlation of GPS observation noise can be efficiently described by means of autoregressive moving average (ARMA) processes. Furthermore, this work includes an up-to-date overview of the GNSS error effects and a comprehensive description of various mathematical methods.

## An Introduction to Stochastic Modeling

Author | : Howard M. Taylor,Samuel Karlin |

Publsiher | : Academic Press |

Total Pages | : 410 |

Release | : 2014-05-10 |

ISBN 10 | : 1483269272 |

ISBN 13 | : 9781483269276 |

Language | : EN, FR, DE, ES & NL |

**An Introduction to Stochastic Modeling Book Review:**

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.

## Stochastic Models in Reliability Engineering

Author | : Lirong Cui,Ilia Frenkel,Anatoly Lisnianski |

Publsiher | : CRC Press |

Total Pages | : 464 |

Release | : 2020-09-01 |

ISBN 10 | : 1000094618 |

ISBN 13 | : 9781000094619 |

Language | : EN, FR, DE, ES & NL |

**Stochastic Models in Reliability Engineering Book Review:**

This book is a collective work by many leading scientists, analysts, mathematicians, and engineers who have been working at the front end of reliability science and engineering. The book covers conventional and contemporary topics in reliability science, all of which have seen extended research activities in recent years. The methods presented in this book are real-world examples that demonstrate improvements in essential reliability and availability for industrial equipment such as medical magnetic resonance imaging, power systems, traction drives for a search and rescue helicopter, and air conditioning systems. The book presents real case studies of redundant multi-state air conditioning systems for chemical laboratories and covers assessments of reliability and fault tolerance and availability calculations. Conventional and contemporary topics in reliability engineering are discussed, including degradation, networks, and dynamic reliability, resilience, and multi-state systems, all of which are relatively new topics to the field. The book is aimed at engineers and scientists, as well as postgraduate students involved in reliability design, analysis, and experiments and applied probability and statistics.

## An Introduction to Stochastic Modeling

Author | : Mark Pinsky,Samuel Karlin |

Publsiher | : Academic Press |

Total Pages | : 563 |

Release | : 2011 |

ISBN 10 | : 0123814162 |

ISBN 13 | : 9780123814166 |

Language | : EN, FR, DE, ES & NL |

**An Introduction to Stochastic Modeling Book Review:**

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, Fourth 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. New to this edition: Realistic applications from a variety of disciplines integrated throughout the text, including more biological applications Plentiful, completely updated problems Completely updated and reorganized end-of-chapter exercise sets, 250 exercises with answers New chapters of stochastic differential equations and Brownian motion and related processes Additional sections on Martingale and Poisson process Realistic applications from a variety of disciplines integrated throughout the text Extensive end of chapter exercises sets, 250 with answers Chapter 1-9 of the new edition are identical to the previous edition New! Chapter 10 - Random Evolutions New! Chapter 11- Characteristic functions and Their Applications

## Stochastic Modelling for Systems Biology Third Edition

Author | : Darren J. Wilkinson |

Publsiher | : CRC Press |

Total Pages | : 384 |

Release | : 2018-12-07 |

ISBN 10 | : 135100090X |

ISBN 13 | : 9781351000901 |

Language | : EN, FR, DE, ES & NL |

**Stochastic Modelling for Systems Biology Third Edition Book Review:**

Since the first edition of Stochastic Modelling for Systems Biology, there have been many interesting developments in the use of "likelihood-free" methods of Bayesian inference for complex stochastic models. Having been thoroughly updated to reflect this, this third edition covers everything necessary for a good appreciation of stochastic kinetic modelling of biological networks in the systems biology context. New methods and applications are included in the book, and the use of R for practical illustration of the algorithms has been greatly extended. There is a brand new chapter on spatially extended systems, and the statistical inference chapter has also been extended with new methods, including approximate Bayesian computation (ABC). Stochastic Modelling for Systems Biology, Third Edition is now supplemented by an additional software library, written in Scala, described in a new appendix to the book. New in the Third Edition New chapter on spatially extended systems, covering the spatial Gillespie algorithm for reaction diffusion master equation models in 1- and 2-d, along with fast approximations based on the spatial chemical Langevin equation Significantly expanded chapter on inference for stochastic kinetic models from data, covering ABC, including ABC-SMC Updated R package, including code relating to all of the new material New R package for parsing SBML models into simulatable stochastic Petri net models New open-source software library, written in Scala, replicating most of the functionality of the R packages in a fast, compiled, strongly typed, functional language Keeping with the spirit of earlier editions, all of the new theory is presented in a very informal and intuitive manner, keeping the text as accessible as possible to the widest possible readership. An effective introduction to the area of stochastic modelling in computational systems biology, this new edition adds additional detail and computational methods that will provide a stronger foundation for the development of more advanced courses in stochastic biological modelling.

## Stochastic Processes and Models in Operations Research

Author | : Anbazhagan, Neelamegam |

Publsiher | : IGI Global |

Total Pages | : 338 |

Release | : 2016-03-24 |

ISBN 10 | : 1522500456 |

ISBN 13 | : 9781522500452 |

Language | : EN, FR, DE, ES & NL |

**Stochastic Processes and Models in Operations Research Book Review:**

Decision-making is an important task no matter the industry. Operations research, as a discipline, helps alleviate decision-making problems through the extraction of reliable information related to the task at hand in order to come to a viable solution. Integrating stochastic processes into operations research and management can further aid in the decision-making process for industrial and management problems. Stochastic Processes and Models in Operations Research emphasizes mathematical tools and equations relevant for solving complex problems within business and industrial settings. This research-based publication aims to assist scholars, researchers, operations managers, and graduate-level students by providing comprehensive exposure to the concepts, trends, and technologies relevant to stochastic process modeling to solve operations research problems.

## Stochastic Modelling and Control

Author | : Mark Davis |

Publsiher | : Springer Science & Business Media |

Total Pages | : 394 |

Release | : 2013-03-08 |

ISBN 10 | : 940094828X |

ISBN 13 | : 9789400948280 |

Language | : EN, FR, DE, ES & NL |

**Stochastic Modelling and Control Book Review:**

This book aims to provide a unified treatment of input/output modelling and of control for discrete-time dynamical systems subject to random disturbances. The results presented are of wide applica bility in control engineering, operations research, econometric modelling and many other areas. There are two distinct approaches to mathematical modelling of physical systems: a direct analysis of the physical mechanisms that comprise the process, or a 'black box' approach based on analysis of input/output data. The second approach is adopted here, although of course the properties ofthe models we study, which within the limits of linearity are very general, are also relevant to the behaviour of systems represented by such models, however they are arrived at. The type of system we are interested in is a discrete-time or sampled-data system where the relation between input and output is (at least approximately) linear and where additive random dis turbances are also present, so that the behaviour of the system must be investigated by statistical methods. After a preliminary chapter summarizing elements of probability and linear system theory, we introduce in Chapter 2 some general linear stochastic models, both in input/output and state-space form. Chapter 3 concerns filtering theory: estimation of the state of a dynamical system from noisy observations. As well as being an important topic in its own right, filtering theory provides the link, via the so-called innovations representation, between input/output models (as identified by data analysis) and state-space models, as required for much contemporary control theory.

## Deterministic Versus Stochastic Modelling in Biochemistry and Systems Biology

Author | : Paola Lecca,Ian Laurenzi,Ferenc Jordan |

Publsiher | : Elsevier |

Total Pages | : 390 |

Release | : 2013-04-09 |

ISBN 10 | : 1908818212 |

ISBN 13 | : 9781908818218 |

Language | : EN, FR, DE, ES & NL |

**Deterministic Versus Stochastic Modelling in Biochemistry and Systems Biology Book Review:**

Stochastic kinetic methods are currently considered to be the most realistic and elegant means of representing and simulating the dynamics of biochemical and biological networks. Deterministic versus stochastic modelling in biochemistry and systems biology introduces and critically reviews the deterministic and stochastic foundations of biochemical kinetics, covering applied stochastic process theory for application in the field of modelling and simulation of biological processes at the molecular scale. Following an overview of deterministic chemical kinetics and the stochastic approach to biochemical kinetics, the book goes onto discuss the specifics of stochastic simulation algorithms, modelling in systems biology and the structure of biochemical models. Later chapters cover reaction-diffusion systems, and provide an analysis of the Kinfer and BlenX software systems. The final chapter looks at simulation of ecodynamics and food web dynamics. Introduces mathematical concepts and formalisms of deterministic and stochastic modelling through clear and simple examples Presents recently developed discrete stochastic formalisms for modelling biological systems and processes Describes and applies stochastic simulation algorithms to implement a stochastic formulation of biochemical and biological kinetics

## Stochastic Processes

Author | : Robert G. Gallager |

Publsiher | : Cambridge University Press |

Total Pages | : 568 |

Release | : 2013-12-12 |

ISBN 10 | : 1107435315 |

ISBN 13 | : 9781107435315 |

Language | : EN, FR, DE, ES & NL |

**Stochastic Processes Book Review:**

This definitive textbook provides a solid introduction to discrete and continuous stochastic processes, tackling a complex field in a way that instils a deep understanding of the relevant mathematical principles, and develops an intuitive grasp of the way these principles can be applied to modelling real-world systems. It includes a careful review of elementary probability and detailed coverage of Poisson, Gaussian and Markov processes with richly varied queuing applications. The theory and applications of inference, hypothesis testing, estimation, random walks, large deviations, martingales and investments are developed. Written by one of the world's leading information theorists, evolving over twenty years of graduate classroom teaching and enriched by over 300 exercises, this is an exceptional resource for anyone looking to develop their understanding of stochastic processes.

## Stochastic Models in Biology

Author | : Narendra S. Goel,Nira Richter-Dyn |

Publsiher | : Elsevier |

Total Pages | : 282 |

Release | : 2016-01-26 |

ISBN 10 | : 1483278107 |

ISBN 13 | : 9781483278100 |

Language | : EN, FR, DE, ES & NL |

**Stochastic Models in Biology Book Review:**

Stochastic Models in Biology describes the usefulness of the theory of stochastic process in studying biological phenomena. The book describes analysis of biological systems and experiments though probabilistic models rather than deterministic methods. The text reviews the mathematical analyses for modeling different biological systems such as the random processes continuous in time and discrete in state space. The book also discusses population growth and extinction through Malthus' law and the work of MacArthur and Wilson. The text then explains the dynamics of a population of interacting species. The book also addresses population genetics under systematic evolutionary pressures known as deterministic equations and genetic changes in a finite population known as stochastic equations. The text then turns to stochastic modeling of biological systems at the molecular level, particularly the kinetics of biochemical reactions. The book also presents various useful equations such as the differential equation for generating functions for birth and death processes. The text can prove valuable for biochemists, cellular biologists, and researchers in the medical and chemical field who are tasked to perform data analysis.

## Stochastic Models Statistics and Their Applications

Author | : Ansgar Steland,Ewaryst Rafajłowicz,Ostap Okhrin |

Publsiher | : Springer Nature |

Total Pages | : 450 |

Release | : 2019-10-15 |

ISBN 10 | : 3030286657 |

ISBN 13 | : 9783030286651 |

Language | : EN, FR, DE, ES & NL |

**Stochastic Models Statistics and Their Applications Book Review:**

This volume presents selected and peer-reviewed contributions from the 14th Workshop on Stochastic Models, Statistics and Their Applications, held in Dresden, Germany, on March 6-8, 2019. Addressing the needs of theoretical and applied researchers alike, the contributions provide an overview of the latest advances and trends in the areas of mathematical statistics and applied probability, and their applications to high-dimensional statistics, econometrics and time series analysis, statistics for stochastic processes, statistical machine learning, big data and data science, random matrix theory, quality control, change-point analysis and detection, finance, copulas, survival analysis and reliability, sequential experiments, empirical processes, and microsimulations. As the book demonstrates, stochastic models and related statistical procedures and algorithms are essential to more comprehensively understanding and solving present-day problems arising in e.g. the natural sciences, machine learning, data science, engineering, image analysis, genetics, econometrics and finance.

## Stochastic Models of Financial Mathematics

Author | : Vigirdas Mackevicius |

Publsiher | : Elsevier |

Total Pages | : 130 |

Release | : 2016-11-08 |

ISBN 10 | : 0081020864 |

ISBN 13 | : 9780081020869 |

Language | : EN, FR, DE, ES & NL |

**Stochastic Models of Financial Mathematics Book Review:**

This book presents a short introduction to continuous-time financial models. An overview of the basics of stochastic analysis precedes a focus on the Black-Scholes and interest rate models. Other topics covered include self-financing strategies, option pricing, exotic options and risk-neutral probabilities. Vasicek, Cox-Ingersoll-Ross, and Heath-Jarrow-Morton interest rate models are also explored. The author presents practitioners with a basic introduction, with more rigorous information provided for mathematicians. The reader is assumed to be familiar with the basics of probability theory. Some basic knowledge of stochastic integration and differential equations theory is preferable, although all preliminary information is given in the first part of the book. Some relatively simple theoretical exercises are also provided. About continuous-time stochastic models of financial mathematics Black-Sholes model and interest rate models Requiring a minimum knowledge of stochastic integration and stochastic differential equations

## Stochastic Modeling of Scientific Data

Author | : Peter Guttorp |

Publsiher | : CRC Press |

Total Pages | : 384 |

Release | : 2018-03-29 |

ISBN 10 | : 135141366X |

ISBN 13 | : 9781351413664 |

Language | : EN, FR, DE, ES & NL |

**Stochastic Modeling of Scientific Data Book Review:**

Stochastic Modeling of Scientific Data combines stochastic modeling and statistical inference in a variety of standard and less common models, such as point processes, Markov random fields and hidden Markov models in a clear, thoughtful and succinct manner. The distinguishing feature of this work is that, in addition to probability theory, it contains statistical aspects of model fitting and a variety of data sets that are either analyzed in the text or used as exercises. Markov chain Monte Carlo methods are introduced for evaluating likelihoods in complicated models and the forward backward algorithm for analyzing hidden Markov models is presented. The strength of this text lies in the use of informal language that makes the topic more accessible to non-mathematicians. The combinations of hard science topics with stochastic processes and their statistical inference puts it in a new category of probability textbooks. The numerous examples and exercises are drawn from astronomy, geology, genetics, hydrology, neurophysiology and physics.

## An Introduction to Continuous Time Stochastic Processes

Author | : Vincenzo Capasso,David Bakstein |

Publsiher | : Springer Science & Business Media |

Total Pages | : 344 |

Release | : 2008-01-03 |

ISBN 10 | : 9780817644284 |

ISBN 13 | : 0817644288 |

Language | : EN, FR, DE, ES & NL |

**An Introduction to Continuous Time Stochastic Processes Book Review:**

This concisely written book is a rigorous and self-contained introduction to the theory of continuous-time stochastic processes. Balancing theory and applications, the authors use stochastic methods and concrete examples to model real-world problems from engineering, biomathematics, biotechnology, and finance. Suitable as a textbook for graduate or advanced undergraduate courses, the work may also be used for self-study or as a reference. The book will be of interest to students, pure and applied mathematicians, and researchers or practitioners in mathematical finance, biomathematics, physics, and engineering.

## Stochastic Modeling of Microstructures

Author | : Kazimierz Sobczyk,David J. Kirkner |

Publsiher | : Springer Science & Business Media |

Total Pages | : 270 |

Release | : 2012-12-06 |

ISBN 10 | : 1461201217 |

ISBN 13 | : 9781461201212 |

Language | : EN, FR, DE, ES & NL |

**Stochastic Modeling of Microstructures Book Review:**

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.

## Stochastic Models Statistics and Their Applications

Author | : Ansgar Steland,Ewaryst Rafajłowicz,Krzysztof Szajowski |

Publsiher | : Springer |

Total Pages | : 492 |

Release | : 2015-02-04 |

ISBN 10 | : 3319138812 |

ISBN 13 | : 9783319138817 |

Language | : EN, FR, DE, ES & NL |

**Stochastic Models Statistics and Their Applications Book Review:**

This volume presents the latest advances and trends in stochastic models and related statistical procedures. Selected peer-reviewed contributions focus on statistical inference, quality control, change-point analysis and detection, empirical processes, time series analysis, survival analysis and reliability, statistics for stochastic processes, big data in technology and the sciences, statistical genetics, experiment design, and stochastic models in engineering. Stochastic models and related statistical procedures play an important part in furthering our understanding of the challenging problems currently arising in areas of application such as the natural sciences, information technology, engineering, image analysis, genetics, energy and finance, to name but a few. This collection arises from the 12th Workshop on Stochastic Models, Statistics and Their Applications, Wroclaw, Poland.

## Stochastic Modeling of Manufacturing Systems

Author | : George Liberopoulos,Chrissoleon T. Papadopoulos,Barış Tan,James MacGregor Smith,Stanley B. Gershwin |

Publsiher | : Springer Science & Business Media |

Total Pages | : 363 |

Release | : 2005-12-12 |

ISBN 10 | : 3540290575 |

ISBN 13 | : 9783540290575 |

Language | : EN, FR, DE, ES & NL |

**Stochastic Modeling of Manufacturing Systems Book Review:**

Manufacturing systems rarely perform exactly as expected and predicted. Unexpected events, such as order changes, equipment failures and product defects, affect the performance of the system and complicate decision-making. This volume is devoted to the development of analytical methods aiming at responding to variability in a way that limits its corrupting effects on system performance. The book includes fifteen novel chapters that mostly focus on the development and analysis of performance evaluation models of manufacturing systems using decomposition-based methods, Markovian and queuing analysis, simulation, and inventory control approaches. They are organized into four distinct sections to reflect their shared viewpoints: factory design, unreliable production lines, queuing network models, production planning and assembly.

## Stochastic Models of Buying Behavior

Author | : William F. Massy,David Bruce Montgomery,Donald G. Morrison,Donald Graham Morrison |

Publsiher | : Mit Press |

Total Pages | : 464 |

Release | : 1970 |

ISBN 10 | : |

ISBN 13 | : STANFORD:36105033952669 |

Language | : EN, FR, DE, ES & NL |

**Stochastic Models of Buying Behavior Book Review:**

Approaches to stochastic modeling; Estimating and testing stochastic models; Brand-choice models; Zero-order models; Two state markov models; Linear learning models for brand choice; A probability diffusion model; Application of the probability diffusion model; Purchase incidence models; Models for purchase timing and market penetration; A stochastic model for monitoring new product adoption; Parameter estimations and some emperical results for STEAM; Extension to STEAM.