"Risk or uncertainty assessments are used as aids to decision making in nearly every aspect of business, education, and government. As a follow-up to the author's bestselling Risk Assessment and Decision Making in Business and Industry: A Practical Guide, Risk Modeling for Determining Value and Decision Making presents comprehensive examples of risk/uncertainty analyses from a broad range of applications. Decision/option selection

Galton used quantiles more than a hundred years ago in describing data. Tukey and Parzen used them in the 60s and 70s in describing populations. Since then, the authors of many papers, both theoretical and practical, have used various aspects of quantiles in their work. Until now, however, no one put all the ideas together to form what turns out to be a general approach to statistics. Statistical Modelling with Quantile Functions does just that. It systematically examines the entire process of statistical modelling, starting with using the quantile function to define continuous distributions. The author shows that by using this approach, it becomes possible to develop complex distributional models from simple components. A modelling kit can be developed that applies to the whole model - deterministic and stochastic components - and this kit operates by adding, multiplying, and transforming distributions rather than data. Statistical Modelling with Quantile Functions adds a new dimension to the practice of statistical modelling that will be of value to anyone faced with analyzing data. Not intended to replace classical approaches but to supplement them, it will make some of the traditional topics easier and clearer, and help readers build and investigate models for their own practical statistical problems.

Large observational studies involving research questions that require the measurement of several features on each individual arise in many fields including the social and medical sciences. This book sets out both the general concepts and the more technical statistical issues involved in analysis and interpretation. Numerous illustrative examples are described in outline and four studies are discussed in some detail. The use of graphical representations of dependencies and independencies among the features under study is stressed, both to incorporate available knowledge at the planning stage of an analysis and to summarize aspects important for interpretation after detailed statistical analysis is complete. This book is aimed at research workers using statistical methods as well as statisticians involved in empirical research.

Dynamo theory, the study of the generation and maintenance of magnetic fields by fluid motions, is important in many areas of physics, ranging from stellar and galactic dynamics, through solar physics and geomagnetism, to reactor physics. This volume contains the lectures given by leading specialists, for an intensive course held at the Newton Institute, as part of a NATO Advanced Study Institute. The course was intended for beginning graduate students, so starts with background material, before leading on to describe areas closer to current research. Topics covered include planetary and solar dynamos, fast dynamos and the use of symmetry principles to derive evolution equations. Detailed bibliographies are provided. As the only modern introduction to the subject, this will be welcome reading for students in planetary and solar physics, plasma physics and astrophysics.

Hormones as Tokens of Selection addresses deep questions in biology: How are biological systems controlled? How can one formulate general theories of homeostasis and control and instantiate such theories in mathematical models? How can one use evolutionary arguments to guide our answers to these questions, recognising that the control mechanisms themselves are a product of evolution? Biological systems are exceptionally varied and extremely difficult to understand, because they are complex and experimentation remains limited relative to the challenges at hand. Moreover, biological phenomena occur at a wide range of temporal and spatial scales. Such a deeply convoluted subject calls for a unifying and coherent theoretical foundation - one which recognises and departs from the primary importance of mathematical modelling and key physicochemical principles to theory formation in the life sciences. This Focus monograph proposes and outlines such a foundation, departing from the deceptively simple proposition that hormones are tokens of evolutionary pressures. Features Provides a coherent and unified approach to a multifaceted problem Pays close attention to both the biological and mathematical modelling aspects of the subject matter, exploring the philosophical background where appropriate Written in a concise and innovative style

As interest in theoretical biology grows, so does the need for an accessible link between these theories and experiments. The central purpose of this book is to illustrate the premise that examination of the kinetics of biological processes can give valuable information concerning the underlying mechanisms that are responsible for these processes. Topics covered range from co-operativity in protein binding, through receptor-infector coupling, to theories of biochemical oscillations in yeast and slime mould. In addition, an introduction to the explosively growing theoretical topic of chaos details attempts to apply this theory in physiology. The material of this book originally appeared as part of the volume Mathematical Models in Molecular and Cellular Biology (edited by L. A. Segel). However each article has been revised and updated.

"If Augustin Cournot had still been alive, he could have won the Nobel Memorial Prize in Economics on at least three different occasions", exclaimed Nobel Laureate Robert Aumann during the 2005 Cournot Centre conference. From his earliest publications, Cournot broke from tradition with his predecessors in applying mathematical modelling to the social sphere. Consequently, he was the first to affirm the mathematization of social phenomena as an essential principle. The fecundity of Cournot's works stems not only from this departure, but also from a richness that irrigated the social sciences of the twentieth century. In this collection, the contributors - including two Nobel laureates in economics - highlight Cournot's profound innovativeness and continued relevance in the areas of industrial economics, mathematical economics, market competition, game theory and epistemology of probability and statistics. Each of the seven authors reminds us of the force and modernity of Cournot's thought as a mathematician, historian of the sciences, philosopher and, not least, as an economist. Combining an epistemological perspective with a theoretical one, this book will be of great interest to researchers and students in the fields of economics, the history of economic thought, and epistemology.

Business Process Modeling, Simulation and Design, Third Edition provides students with a comprehensive coverage of a range of analytical tools used to model, analyze, understand, and ultimately design business processes. The new edition of this very successful textbook includes a wide range of approaches such as graphical flowcharting tools, cycle time and capacity analyses, queuing models, discrete-event simulation, simulation-optimization, and data mining for process analytics. While most textbooks on business process management either focus on the intricacies of computer simulation or managerial aspects of business processes, this textbook does both. It presents the tools to design business processes and management techniques on operating them efficiently. The book focuses on the use of discrete event simulation as the main tool for analyzing, modeling, and designing effective business processes. The integration of graphic user-friendly simulation software enables a systematic approach to create optimal designs.

This monograph provides a framework for students and practitioners who are working on the solution of electromagnetic imaging in geophysics. Bridging the gap between theory and practical applied material (for example, inverse and forward problems), it provides:* A simple explanation of finite volume discretization.* Basic concepts in solving inverse problems through optimization.* A summary of applied electromagnetics methods.* MATLAB? code for efficient computation.

Probabilistic Foundations of Statistical Network Analysis presents a fresh and insightful perspective on the fundamental tenets and major challenges of modern network analysis. Its lucid exposition provides necessary background for understanding the essential ideas behind exchangeable and dynamic network models, network sampling, and network statistics such as sparsity and power law, all of which play a central role in contemporary data science and machine learning applications. The book rewards readers with a clear and intuitive understanding of the subtle interplay between basic principles of statistical inference, empirical properties of network data, and technical concepts from probability theory. Its mathematically rigorous, yet non-technical, exposition makes the book accessible to professional data scientists, statisticians, and computer scientists as well as practitioners and researchers in substantive fields. Newcomers and non-quantitative researchers will find its conceptual approach invaluable for developing intuition about technical ideas from statistics and probability, while experts and graduate students will find the book a handy reference for a wide range of new topics, including edge exchangeability, relative exchangeability, graphon and graphex models, and graph-valued Levy process and rewiring models for dynamic networks. The author's incisive commentary supplements these core concepts, challenging the reader to push beyond the current limitations of this emerging discipline. With an approachable exposition and more than 50 open research problems and exercises with solutions, this book is ideal for advanced undergraduate and graduate students interested in modern network analysis, data science, machine learning, and statistics. Harry Crane is Associate Professor and Co-Director of the Graduate Program in Statistics and Biostatistics and an Associate Member of the Graduate Faculty in Philosophy at Rutgers University. Professor Crane's research interests cover a range of mathematical and applied topics in network science, probability theory, statistical inference, and mathematical logic. In addition to his technical work on edge and relational exchangeability, relative exchangeability, and graph-valued Markov processes, Prof. Crane's methods have been applied to domain-specific cybersecurity and counterterrorism problems at the Foreign Policy Research Institute and RAND's Project AIR FORCE.

Originally published in 1984. Since the logic underlying economic theory can only be grasped fully by a thorough understanding of the mathematics, this book will be invaluable to economists wishing to understand vast areas of important research. It provides a basic introduction to the fundamental mathematical ideas of topology and calculus, and uses these to present modern singularity theory and recent results on the generic existence of isolated price equilibria in exchange economies.

This book is an introduction to numerical analysis in geomechanics and is intended for advanced undergraduate and beginning graduate study of the mechanics of porous, jointed rocks and soils. Although familiarity with the concepts of stress, strain and so on is assumed, a review of the fundamentals of solid mechanics including concepts of physical laws, kinematics and material laws is presented in an appendix. Emphasis is on the popular finite element method but brief explanations of the boundary element method, the distinct element method (also known as the discrete element method) and discontinuous deformation analysis are included. Familiarity with a computer programming language such as Fortran, C++ or Python is not required, although programming excerpts in Fortran are presented at the end of some chapters. This work begins with an intuitive approach to interpolation over a triangular element and thus avoids making the simple complex by not doing energy minimization via a calculus of variations approach so often found in reference books on the finite element method. The presentation then proceeds to a principal of virtual work via the well-known divergence theorem to obtain element equilibrium and then global equilibrium, both expressed as stiffness equations relating force to displacement. Solution methods for the finite element approach including elimination and iteration methods are discussed. Hydro-mechanical coupling is described and extension of the finite element method to accommodate fluid flow in porous geological media is made. Example problems illustrate important concepts throughout the text. Additional problems for a 15-week course of study are presented in an appendix; solutions are given in another appendix.

Model theory investigates mathematical structures by means of formal languages. These so-called first-order languages have proved particularly useful. The text introduces the reader to the model theory of first-order logic, avoiding syntactical issues that are not too relevant to model-theory. In this spirit, the compactness theorem is proved via the algebraically useful ultraproduct technique, rather than via the completeness theorem of first-order logic. This leads fairly quickly to algebraic applications, like Malcev's local theorems (of group theory) and, after a little more preparation, also to Hilbert's Nullstellensatz (of field theory). Steinitz' dimension theory for field extensions is obtained as a special case of a much more general model-theoretic treatment of strongly minimal sets. The final chapter is on the models of the first-order theory of the integers as an abelian group. This material appears here for the first time in a textbook of introductory level, and is used to give hints to further reading and to recent developments in the field, such as stability (or classification) theory. The latter itself is not touched upon. The undergraduate or graduate, is assumed t

A ubiquitous tool in mathematical biology and chemical engineering, the chemostat often produces instabilities that pose safety hazards and adversely affect the optimization of bioreactive systems. Singularity theory and bifurcation diagrams together offer a useful framework for addressing these issues. Based on the authors' extensive work in this field, Dynamics of the Chemostat: A Bifurcation Theory Approach explores the use of bifurcation theory to analyze the static and dynamic behavior of the chemostat. IntroductionThe authors first survey the major work that has been carried out on the stability of continuous bioreactors. They next present the modeling approaches used for bioreactive systems, the different kinetic expressions for growth rates, and tools, such as multiplicity, bifurcation, and singularity theory, for analyzing nonlinear systems. ApplicationThe text moves on to the static and dynamic behavior of the basic unstructured model of the chemostat for constant and variable yield coefficients as well as in the presence of wall attachment. It then covers the dynamics of interacting species, including pure and simple microbial competition, biodegradation of mixed substrates, dynamics of plasmid-bearing and plasmid-free recombinant cultures, and dynamics of predator-prey interactions. The authors also examine dynamics of the chemostat with product formation for various growth models, provide examples of bifurcation theory for studying the operability and dynamics of continuous bioreactor models, and apply elementary concepts of bifurcation theory to analyze the dynamics of a periodically forced bioreactor. Using singularity theory and bifurcation techniques, this book presents a cohesive mathematical framework for analyzing and modeling the macro- and microscopic interactions occurring in chemostats. The text includes models that describe the intracellular and operating elements of the bioreactive system. It also explains the mathematical theory behind the models.

Introduction to Mathematical Modeling and Computer Simulations is written as a textbook for readers who want to understand the main principles of Modeling and Simulations in settings that are important for the applications, without using the profound mathematical tools required by most advanced texts. It can be particularly useful for applied mathematicians and engineers who are just beginning their careers. The goal of this book is to outline Mathematical Modeling using simple mathematical descriptions, making it accessible for first- and second-year students. Chapter 1 and the Preface of this book is freely available as a downloadable Open Access PDF under a Creative Commons Attribution-Non Commercial-No Derivatives 4.0 license available at http://www.taylorfrancis.com/books/e/9781315277240

Molecular biologists are performing increasingly large and complicated experiments, but often have little background in data analysis. The book is devoted to teaching the statistical and computational techniques molecular biologists need to analyze their data. It explains the big-picture concepts in data analysis using a wide variety of real-world molecular biological examples such as eQTLs, ortholog identification, motif finding, inference of population structure, protein fold prediction and many more. The book takes a pragmatic approach, focusing on techniques that are based on elegant mathematics yet are the simplest to explain to scientists with little background in computers and statistics.

This book is designed primarily for upper level undergraduate and graduate level students taking a course in multilevel modelling and/or statistical modelling with a large multilevel modelling component. The focus is on presenting the theory and practice of major multilevel modelling techniques in a variety of contexts, using Mplus as the software tool, and demonstrating the various functions available for these analyses in Mplus, which is widely used by researchers in various fields, including most of the social sciences. In particular, Mplus offers users a wide array of tools for latent variable modelling, including for multilevel data.

This new book focuses on nanomaterial development as well as investigations of combustion and explosion processes. It presents valuable information on the modeling of processes and on quantum chemical calculations and leading-edge research from around the world in this dynamic field, focusing on concepts above formal experimental techniques and theoretical methods of chemical physics for micro- and nanotechnologies. Also presented are non-linear kinetic appearances and their possible applications.

The book provides an introduction to deterministic (and some stochastic) modeling of spatiotemporal phenomena in ecology, epidemiology, and neural systems. A survey of the classical models in the fields with up to date applications is given. The book begins with detailed description of how spatial dynamics/diffusive processes influence the dynamics of biological populations. These processes play a key role in understanding the outbreak and spread of pandemics which help us in designing the control strategies from the public health perspective. A brief discussion on the functional mechanism of the brain (single neuron models and network level) with classical models of neuronal dynamics in space and time is given. Relevant phenomena and existing modeling approaches in ecology, epidemiology and neuroscience are introduced, which provide examples of pattern formation in these models. The analysis of patterns enables us to study the dynamics of macroscopic and microscopic behaviour of underlying systems and travelling wave type patterns observed in dispersive systems. Moving on to virus dynamics, authors present a detailed analysis of different types models of infectious diseases including two models for influenza, five models for Ebola virus and seven models for Zika virus with diffusion and time delay. A Chapter is devoted for the study of Brain Dynamics (Neural systems in space and time). Significant advances made in modeling the reaction-diffusion systems are presented and spatiotemporal patterning in the systems is reviewed. Development of appropriate mathematical models and detailed analysis (such as linear stability, weakly nonlinear analysis, bifurcation analysis, control theory, numerical simulation) are presented. Key Features Covers the fundamental concepts and mathematical skills required to analyse reaction-diffusion models for biological populations. Concepts are introduced in such a way that readers with a basic knowledge of differential equations and numerical methods can understand the analysis. The results are also illustrated with figures. Focuses on mathematical modeling and numerical simulations using basic conceptual and classic models of population dynamics, Virus and Brain dynamics. Covers wide range of models using spatial and non-spatial approaches. Covers single, two and multispecies reaction-diffusion models from ecology and models from bio-chemistry. Models are analysed for stability of equilibrium points, Turing instability, Hopf bifurcation and pattern formations. Uses Mathematica for problem solving and MATLAB for pattern formations. Contains solved Examples and Problems in Exercises. The Book is suitable for advanced undergraduate, graduate and research students. For those who are working in the above areas, it provides information from most of the recent works. The text presents all the fundamental concepts and mathematical skills needed to build models and perform analyses.

This hands-on introduction to numerical geodynamic modelling provides a solid grounding in the necessary mathematical theory and techniques, including continuum mechanics and partial differential equations, before introducing key numerical modelling methods and applications. Fully updated, this second edition includes four completely new chapters covering the most recent advances in modelling inertial processes, seismic cycles and fluid-solid interactions, and the development of adaptive mesh refinement algorithms. Many well-documented, state-of-the-art visco-elasto-plastic 2D models are presented, which allow robust modelling of key geodynamic processes. Requiring only minimal prerequisite mathematical training, and featuring over sixty practical exercises and ninety MATLAB (R) examples, this user-friendly resource encourages experimentation with geodynamic models. It is an ideal introduction for advanced courses and can be used as a self-study aid for graduates seeking to master geodynamic modelling for their own research projects.

This bestselling text provides a practical guide to structural equation modeling (SEM) using the Amos Graphical approach. Using clear, everyday language, the text is ideal for those with little to no exposure to either SEM or Amos. The author reviews SEM applications based on actual data taken from her own research. Each chapter "walks" readers through the steps involved (specification, estimation, evaluation, and post hoc modification) in testing a variety of SEM models. Accompanying each application is: an explanation of the issues addressed and a schematic presentation of hypothesized model structure; Amos input and output with interpretations; use of the Amos toolbar icons and pull-down menus; and data upon which the model application was based, together with updated references pertinent to the SEM model tested. Thoroughly updated throughout, the new edition features: All new screen shots featuring Amos Version 23. Descriptions and illustrations of Amos' new Tables View format which enables the specification of a structural model in spreadsheet form. Key concepts and/or techniques that introduce each chapter. Alternative approaches to model analyses when enabled by Amos thereby allowing users to determine the method best suited to their data. Provides analysis of the same model based on continuous and categorical data (Ch. 5) thereby enabling readers to observe two ways of specifying and testing the same model as well as compare results. All applications based on the Amos graphical mode interface accompanied by more "how to" coverage of graphical techniques unique to Amos. More explanation of key procedures and analyses that address questions posed by readers All application data files are available at www.routledge.com/9781138797031. The two introductory chapters in Section 1 review the fundamental concepts of SEM methodology and a general overview of the Amos program. Section 2 provides single-group analyses applications including two first-order confirmatory factor analytic (CFA) models, one second-order CFA model, and one full latent variable model. Section 3 presents multiple-group analyses applications with two rooted in the analysis of covariance structures and one in the analysis of mean and covariance structures. Two models that are increasingly popular with SEM practitioners, construct validity and testing change over time using the latent growth curve, are presented in Section 4. The book concludes with a review of the use of bootstrapping to address non-normal data and a review of missing (or incomplete) data in Section 5. An ideal supplement for graduate level courses in psychology, education, business, and social and health sciences that cover the fundamentals of SEM with a focus on Amos, this practical text continues to be a favorite of both researchers and practitioners. A prerequisite of basic statistics through regression analysis is recommended but no exposure to either SEM or Amos is required.

Structured population models are transport-type equations often applied to describe evolution of heterogeneous populations of biological cells, animals or humans, including phenomena such as crowd dynamics or pedestrian flows. This book introduces the mathematical underpinnings of these applications, providing a comprehensive analytical framework for structured population models in spaces of Radon measures. The unified approach allows for the study of transport processes on structures that are not vector spaces (such as traffic flow on graphs) and enables the analysis of the numerical algorithms used in applications. Presenting a coherent account of over a decade of research in the area, the text includes appendices outlining the necessary background material and discusses current trends in the theory, enabling graduate students to jump quickly into research.

The safeguarding of children and young people participating in sport has become an increasingly prominent concern in policy-making and research communities around the world. Major organisations such as the IOC and UNICEF now officially recognize that children in sport can be at risk of exploitation and abuse, and this concern has led to the emergence of new initiatives and policies aimed at protecting vulnerable young people and athletes. This book is the first to comprehensively review contemporary developments in child protection and safeguarding in sport on a global level. The book is divided into two parts. Part One critically analyses current child protection and safeguarding policy and practice in sport across a range of countries, including the US, Canada, the UK, Australia, China and Germany, providing a global context for current policy and practice. This represents the most comprehensive review to date of the landscape of child protection and safeguarding in sport and provides a starting point for critical international comparisons. Part Two explores a range of issues related to child protection and safeguarding in sport, including many not covered in previous books, such as emotional abuse, injury and over-training. While in many instances the impetus for policy in this area has arisen from concerns about sexual abuse, the second part of this book therefore opens up a broader, more holistic approach to child and athlete welfare. By bringing together many of the leading researchers working in child and athlete protection in sport from around the world, this book is important reading for all advanced students, researchers, policy-makers or practitioners working in youth sport, physical education, sports coaching, coach education or child protection.

The book describes a computational model of the immune system reaction, C-ImmSim, built along the lines of the computer model known as the Celada-Seiden model (CS-model). The computational counterpart of the CS-model is called IMMSIM which stands for IMMune system SIMulator. IMMSIM was written in 1992 by the physicist Phil E. Seiden and the immunologist Franco Celada. This model was built around the idea of developing a computerized system to perform experiments similar in vivo experiments; a tool developed to help biologists testing theories and hypothesis about how the immune system works. C-ImmSim is best viewed as a collection of models in a single program. It incorporates the principal core facts of today's immunological knowledge, such as the diversity of specific elements, MHC restriction, clonal selection, thymic education of T cells, antigen processing and presentation (both the cytosolic and endocytic pathways are implemented), cell-cell cooperation, homeostasis of cells created by the bone marrow, hyper mutation of antibodies, maturation of the cellular and humoral response, and memory. Besides, an antigen can represent a bacterium, a virus, or an allergen or a tumor cell. C-ImmSim has been recently customized to simulate the HIV-1 infection. Moreover, it can simulate the immunotherapy for cancer. These features are all present in the code and people can choose to turn them on and off at compiling time. The book presents the basic model as well as the various customizations to implement the description of different diseases and the way they have been used in practice to produce new knowledge either from hypothesis or from lab-experiment data. In this respect, the book can be used as a practical guide to implement a computational model with which to study a specific disease and to try to address realistic clinical questions.

Extreme Value Modeling and Risk Analysis: Methods and Applications presents a broad overview of statistical modeling of extreme events along with the most recent methodologies and various applications. The book brings together background material and advanced topics, eliminating the need to sort through the massive amount of literature on the subject. After reviewing univariate extreme value analysis and multivariate extremes, the book explains univariate extreme value mixture modeling, threshold selection in extreme value analysis, and threshold modeling of non-stationary extremes. It presents new results for block-maxima of vine copulas, develops time series of extremes with applications from climatology, describes max-autoregressive and moving maxima models for extremes, and discusses spatial extremes and max-stable processes. The book then covers simulation and conditional simulation of max-stable processes; inference methodologies, such as composite likelihood, Bayesian inference, and approximate Bayesian computation; and inferences about extreme quantiles and extreme dependence. It also explores novel applications of extreme value modeling, including financial investments, insurance and financial risk management, weather and climate disasters, clinical trials, and sports statistics. Risk analyses related to extreme events require the combined expertise of statisticians and domain experts in climatology, hydrology, finance, insurance, sports, and other fields. This book connects statistical/mathematical research with critical decision and risk assessment/management applications to stimulate more collaboration between these statisticians and specialists.