Bayesian Modeling Using WinBUGS

Probabilistic Finite Element Model Updating Using Bayesian Statistics: Applications to Aeronautical and Mechanical Engineering Tshilidzi Marwala and Ilyes Boulkaibet, University of Johannesburg, South Africa Sondipon Adhikari, Swansea University, UK Covers the probabilistic finite element model based on Bayesian statistics with applications to aeronautical and mechanical engineering Finite element models are used widely to model the dynamic behaviour of many systems including in electrical, aerospace and mechanical engineering. The book covers probabilistic finite element model updating, achieved using Bayesian statistics. The Bayesian framework is employed to estimate the probabilistic finite element models which take into account of the uncertainties in the measurements and the modelling procedure. The Bayesian formulation achieves this by formulating the finite element model as the posterior distribution of the model given the measured data within the context of computational statistics and applies these in aeronautical and mechanical engineering. Probabilistic Finite Element Model Updating Using Bayesian Statistics contains simple explanations of computational statistical techniques such as Metropolis-Hastings Algorithm, Slice sampling, Markov Chain Monte Carlo method, hybrid Monte Carlo as well as Shadow Hybrid Monte Carlo and their relevance in engineering. Key features: Contains several contributions in the area of model updating using Bayesian techniques which are useful for graduate students. Explains in detail the use of Bayesian techniques to quantify uncertainties in mechanical structures as well as the use of Markov Chain Monte Carlo techniques to evaluate the Bayesian formulations. The book is essential reading for researchers, practitioners and students in mechanical and aerospace engineering.

Cutting-edge techniques for groundwater modeling using GIS technology Groundwater Modeling Using Geographical Information Systems covers fundamental information on flow and mass transport modeling and demonstrates how GIS technology makes these models and analyses more accurate than ever before. GIS technology allows for swift organization, quantification, and interpretation of large quantities of geohydrological data with computer accuracy and minimal risk of human error. This book's companion Web site provides the Princeton Transport Code, as well as the plug-in extensions required to interface this code with the Argus ONE numerical environment software enclosed with this book. Plug-in extensions for MODFLOW and MT3D computer codes can be found at the Argus ONE Web site (www.argusint.com). The process for using the Geographic Modeling Approach (GMA) to model groundwater flow and transport is demonstrated step by step with a field example from Tucson, Arizona. The GMA is composed of the Argus ONE Geographic Information Modeling system and the Princeton Transport Code groundwater flow and transport model, interfaced through the plug-in extension available on Argus ONE. Enhanced with more than 150 illustrations and screen captures, Groundwater Modeling Using Geographical Information Systems is a fundamental book for civil engineers, hydrologists, environmental engineers, geologists, and students in these fields, as well as software engineers working on GIS applications and environmental attorneys and regulators. When used in combination with the free modeling software, this book provides an excellent student text.

A reference guide for applications of SEM using Mplus Structural Equation Modeling: Applications Using Mplus is intended as both a teaching resource and a reference guide. Written in non-mathematical terms, this book focuses on the conceptual and practical aspects of Structural Equation Modeling (SEM). Basic concepts and examples of various SEM models are demonstrated along with recently developed advanced methods, such as mixture modeling and model-based power analysis and sample size estimate for SEM. The statistical modeling program, Mplus, is also featured and provides researchers with a flexible tool to analyze their data with an easy-to-use interface and graphical displays of data and analysis results. Key features: Presents a useful reference guide for applications of SEM whilst systematically demonstrating various advanced SEM models, such as multi-group and mixture models using Mplus. 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 detail interpretation of Mplus results. Explores different methods for sample size estimate and statistical power analysis for SEM. By following the examples provided in this book, readers will be able to build their own SEM models using Mplus. Teachers, graduate students, and researchers in social sciences and health studies will also benefit from this book.

Provides simplified MATLAB codes for analysis of photovoltaic systems, describes the model of the whole photovoltaic power system, and shows readers how to build these models line by line. This book presents simplified coded models for photovoltaic (PV) based systems using MATLAB to help readers understand the dynamic behavior of these systems. Through the use of MATLAB, the reader has the ability to modify system configuration, parameters and optimization criteria. Topics covered include energy sources, storage, and power electronic devices. This book contains six chapters that cover systems’ components from the solar source to the end-user. Chapter 1 discusses modelling of the solar source, and Chapter 2 discusses modelling of the photovoltaic source. Chapter 3 focuses on modeling of PV systems’ power electronic features and auxiliary power sources. Modeling of PV systems’ energy flow is examined in Chapter 4, while Chapter 5 discusses PV systems in electrical power systems. Chapter 6 presents an application of PV system models in systems’ size optimization. Common control methodologies applied to these systems are also modeled. Covers the basic models of the whole photovoltaic power system, enabling the reader modify the models to provide different sizing and control methodologies Examines auxiliary components to photovoltaic systems, including wind turbines, diesel generators, and pumps Contains examples, drills and codes Modeling of Photovoltaic Systems Using MATLAB: Simplified Green Codes is a reference forresearchers, students, and engineers who work in the field of renewable energy, and specifically in photovoltaic systems.

Comprehensive instruction on developing real-world financial models This book, designed for self-study, classroom use, and reference, presents a com-prehensive approach to developing simple to sophisticated financial models in all major areas of finance. The approach is based on the author's 20 years of experience of developing such models in the business world and teaching a popular MBA class in financial modeling. The book assumes only basic knowledge of Excel and teaches all advanced features of Excel and VBA from scratch using a unique simple method. A companion CD includes all working versions of all the models presented in the book and additional useful reference material. Chandan Sengupta (White Plains, NY) teaches finance in the MBA program at Fordham University's Graduate School of Business. Formerly, he was vice president of the Chase Manhattan Bank for eight years and senior financial advisor for Mobil Corporation for 10 years. He is also the author of The Only Proven Road to Investment Success (0-471-44307-7).

A comprehensive and detailed treatment of the program SIMULINK® that focuses on SIMULINK® for simulations in Digital and Wireless Communications Modeling of Digital Communication Systems Using SIMULINK® introduces the reader to SIMULINK®, an extension of the widely-used MATLAB modeling tool, and the use of SIMULINK® in modeling and simulating digital communication systems, including wireless communication systems. Readers will learn to model a wide selection of digital communications techniques and evaluate their performance for many important channel conditions. Modeling of Digital Communication Systems Using SIMULINK® is organized in two parts. The first addresses Simulink® models of digital communications systems using various modulation, coding, channel conditions and receiver processing techniques. The second part provides a collection of examples, including speech coding, interference cancellation, spread spectrum, adaptive signal processing, Kalman filtering and modulation and coding techniques currently implemented in mobile wireless systems. Covers case examples, progressing from basic to complex Provides applications for mobile communications, satellite communications, and fixed wireless systems that reveal the power of SIMULINK modeling Includes access to useable SIMULINK® simulations online Covering both the use of SIMULINK® in digital communications and the complex aspects of wireless communication systems, Modeling of Digital Communication Systems UsingSIMULINK® is a great resource for both practicing engineers and students with MATLAB experience.

In today's increasingly competitive financial world, successful risk management, portfolio management, and financial structuring demand more than up-to-date financial know-how. They also call for quantitative expertise, including the ability to effectively apply mathematical modeling tools and techniques, in this case credit. Credit Risk Modeling using Excel and VBA with DVD provides practitioners with a hands on introduction to credit risk modeling. Instead of just presenting analytical methods it shows how to implement them using Excel and VBA, in addition to a detailed description in the text a DVD guides readers step by step through the implementation. The authors begin by showing how to use option theoretic and statistical models to estimate a borrowers default risk. The second half of the book is devoted to credit portfolio risk. The authors guide readers through the implementation of a credit risk model, show how portfolio models can be validated or used to access structured credit products like CDO’s. The final chapters address modeling issues associated with the new Basel Accord.

Bridges the gap between electromagnetics and circuits by addressing electrometric modeling (EM) using the Partial Element Equivalent Circuit (PEEC) method This book provides intuitive solutions to electromagnetic problems by using the Partial Element Equivalent Circuit (PEEC) method. This book begins with an introduction to circuit analysis techniques, laws, and frequency and time domain analyses. The authors also treat Maxwell's equations, capacitance computations, and inductance computations through the lens of the PEEC method. Next, readers learn to build PEEC models in various forms: equivalent circuit models, non-orthogonal PEEC models, skin-effect models, PEEC models for dielectrics, incident and radiate field models, and scattering PEEC models. The book concludes by considering issues like stability and passivity, and includes five appendices some with formulas for partial elements. Leads readers to the solution of a multitude of practical problems in the areas of signal and power integrity and electromagnetic interference Contains fundamentals, applications, and examples of the PEEC method Includes detailed mathematical derivations Circuit Oriented Electromagnetic Modeling Using the PEEC Techniques is a reference for students, researchers, and developers who work on the physical layer modeling of IC interconnects and Packaging, PCBs, and high speed links.

This book brings together a collection of articles on statistical methods relating to missing data analysis, including multiple imputation, propensity scores, instrumental variables, and Bayesian inference. Covering new research topics and real-world examples which do not feature in many standard texts. The book is dedicated to Professor Don Rubin (Harvard). Don Rubin has made fundamental contributions to the study of missing data. Key features of the book include: Comprehensive coverage of an imporant area for both research and applications. Adopts a pragmatic approach to describing a wide range of intermediate and advanced statistical techniques. Covers key topics such as multiple imputation, propensity scores, instrumental variables and Bayesian inference. Includes a number of applications from the social and health sciences. Edited and authored by highly respected researchers in the area.

Understand and utilize the latest developments in Weibull inferential methods While the Weibull distribution is widely used in science and engineering, most engineers do not have the necessary statistical training to implement the methodology effectively. Using the Weibull Distribution: Reliability, Modeling, and Inference fills a gap in the current literature on the topic, introducing a self-contained presentation of the probabilistic basis for the methodology while providing powerful techniques for extracting information from data. The author explains the use of the Weibull distribution and its statistical and probabilistic basis, providing a wealth of material that is not available in the current literature. The book begins by outlining the fundamental probability and statistical concepts that serve as a foundation for subsequent topics of coverage, including: • Optimum burn-in, age and block replacement, warranties and renewal theory • Exact inference in Weibull regression • Goodness of fit testing and distinguishing the Weibull from the lognormal • Inference for the Three Parameter Weibull Throughout the book, a wealth of real-world examples showcases the discussed topics and each chapter concludes with a set of exercises, allowing readers to test their understanding of the presented material. In addition, a related website features the author's own software for implementing the discussed analyses along with a set of modules written in Mathcad®, and additional graphical interface software for performing simulations. With its numerous hands-on examples, exercises, and software applications, Using the Weibull Distribution is an excellent book for courses on quality control and reliability engineering at the upper-undergraduate and graduate levels. The book also serves as a valuable reference for engineers, scientists, and business analysts who gather and interpret data that follows the Weibull distribution