Modeling Uncertainty in the Earth Sciences

A valuable guide to understanding the problem of quantifying uncertainty in dose response relations for toxic substances In today's scientific research, there exists the need to address the topic of uncertainty as it pertains to dose response modeling. Uncertainty Modeling in Dose Response is the first book of its kind to implement and compare different methods for quantifying the uncertainty in the probability of response, as a function of dose. This volume gathers leading researchers in the field to properly address the issue while communicating concepts from diverse viewpoints and incorporating valuable insights. The result is a collection that reveals the properties, strengths, and weaknesses that exist in the various approaches to bench test problems. This book works with four bench test problems that were taken from real bioassay data for hazardous substances currently under study by the United States Environmental Protection Agency (EPA). The use of actual data provides readers with information that is relevant and representative of the current work being done in the field. Leading contributors from the toxicology and risk assessment communities have applied their methods to quantify model uncertainty in dose response for each case by employing various approaches, including Benchmark Dose Software methods, probabilistic inversion with isotonic regression, nonparametric Bayesian modeling, and Bayesian model averaging. Each chapter is reviewed and critiqued from three professional points of view: risk analyst/regulator, statistician/mathematician, and toxicologist/epidemiologist. In addition, all methodologies are worked out in detail, allowing readers to replicate these analyses and gain a thorough understanding of the methods. Uncertainty Modeling in Dose Response is an excellent book for courses on risk analysis and biostatistics at the upper-undergraduate and graduate levels. It also serves as a valuable reference for risk assessment, toxicology, biostatistics, and environmental chemistry professionals who wish to expand their knowledge and expertise in statistical dose response modeling problems and approaches.

This edited book is divided into three parts: Fundamentals of Medical and Health Sciences Modeling and Simulation introduces modeling and simulation in the medical and health sciences; Medical and Health Sciences Models provides the theoretical underpinnings of medical and health sciences modeling; and Modeling and Simulation Applications in Medical and Health Sciences focuses on teaching, training, and research applications. The book begins with a general discussion of modeling and simulation from the modeling and simulation discipline perspective. This discussion grounds the reader in common terminology. It also relates this terminology to concepts found in the medical and health care (MHC) area to help bridge the gap between developers and MHC practitioners. Three distinct modes of modeling and simulation are described: live, constructive, and virtual. The live approach explains the concept of using real (live) people employing real equipment for training purposes. The constructive mode is a means of engaging medical modeling and simulation. In constructive simulation, simulated people and simulated equipment are developed to augment real-world conditions for training or experimentation purposes. The virtual mode is perhaps the most fascinating as virtual operating rooms and synthetic training environments are being produced for practitioners and educators at break-neck speed. In this mode, real people are employing simulated equipment to improve physical skills and decision-making ability.

Praise for the First Edition «. . . a readable, comprehensive volume that . . . belongs on the desk, close at hand, of any serious researcher or practitioner.» —Mathematical Geosciences The state of the art in geostatistics Geostatistical models and techniques such as kriging and stochastic multi-realizations exploit spatial correlations to evaluate natural resources, help optimize their development, and address environmental issues related to air and water quality, soil pollution, and forestry. Geostatistics: Modeling Spatial Uncertainty, Second Edition presents a comprehensive, up-to-date reference on the topic, now featuring the latest developments in the field. The authors explain both the theory and applications of geostatistics through a unified treatment that emphasizes methodology. Key topics that are the foundation of geostatistics are explored in-depth, including stationary and nonstationary models; linear and nonlinear methods; change of support; multivariate approaches; and conditional simulations. The Second Edition highlights the growing number of applications of geostatistical methods and discusses three key areas of growth in the field: New results and methods, including kriging very large datasets; kriging with outliers; nonse??parable space-time covariances; multipoint simulations; pluri-gaussian simulations; gradual deformation; and extreme value geostatistics Newly formed connections between geostatistics and other approaches such as radial basis functions, Gaussian Markov random fields, and data assimilation New perspectives on topics such as collocated cokriging, kriging with an external drift, discrete Gaussian change-of-support models, and simulation algorithms Geostatistics, Second Edition is an excellent book for courses on the topic at the graduate level. It also serves as an invaluable reference for earth scientists, mining and petroleum engineers, geophysicists, and environmental statisticians who collect and analyze data in their everyday work.

The first comprehensive synthesis of genomic techniques in earth sciences The past 15 years have witnessed an explosion of DNA sequencing technologies that provide unprecedented insights into biology. Although this technological revolution has been driven by the biomedical sciences, it also offers extraordinary opportunities in the earth and environmental sciences. In particular, the application of «omics» methods (genomics, transcriptomics, proteomics) directly to environmental samples offers exciting new vistas of complex microbial communities and their roles in environmental and geochemical processes. This unique book fills the gap where there exists a lack of resources and infrastructure to educate and train geoscientists about the opportunities, approaches, and analytical methods available in the application of omic technologies to problems in the geosciences. Genomic Approaches in Earth and Environmental Sciences begins by covering the role of microorganisms in earth and environmental processes. It then goes on to discuss how omics approaches provide new windows into geobiological processes. It delves into the DNA sequencing revolution and the impact that genomics has made on the geosciences. The book then discusses the methods used in the field, beginning with an overview of current technologies. After that it offers in-depth coverage of single cell genomics, metagenomics, metatranscriptomics, metaproteomics, and functional approaches, before finishing up with an outlook on the future of the field. The very first synthesis of an important new family of techniques Shows strengths and limitations (both practical and theoretical) of the techniques Deals with both theoretical and laboratory basics Shows use of techniques in a variety of applications, including various aspects of environmental science, geobiology, and evolution Genomic Approaches in Earth and Environmental Sciences is a welcome addition to the library of all earth and environmental scientists and students working within a wide range of subdisciplines.

Managing uncertainties in industrial systems is a daily challenge to ensure improved design, robust operation, accountable performance and responsive risk control. Authored by a leading European network of experts representing a cross section of industries, Uncertainty in Industrial Practice aims to provide a reference for the dissemination of uncertainty treatment in any type of industry. It is concerned with the quantification of uncertainties in the presence of data, model(s) and knowledge about the system, and offers a technical contribution to decision-making processes whilst acknowledging industrial constraints. The approach presented can be applied to a range of different business contexts, from research or early design through to certification or in-service processes. The authors aim to foster optimal trade-offs between literature-referenced methodologies and the simplified approaches often inevitable in practice, owing to data, time or budget limitations of technical decision-makers. Uncertainty in Industrial Practice: Features recent uncertainty case studies carried out in the nuclear, air & space, oil, mechanical and civil engineering industries set in a common methodological framework. Presents methods for organizing and treating uncertainties in a generic and prioritized perspective. Illustrates practical difficulties and solutions encountered according to the level of complexity, information available and regulatory and financial constraints. Discusses best practice in uncertainty modeling, propagation and sensitivity analysis through a variety of statistical and numerical methods. Reviews recent standards, references and available software, providing an essential resource for engineers and risk analysts in a wide variety of industries. This book provides a guide to dealing with quantitative uncertainty in engineering and modelling and is aimed at practitioners, including risk-industry regulators and academics wishing to develop industry-realistic methodologies.

A one-of-a-kind compilation of modern statistical methods designed to support and advance research across the social sciences Statistics in the Social Sciences: Current Methodological Developments presents new and exciting statistical methodologies to help advance research and data analysis across the many disciplines in the social sciences. Quantitative methods in various subfields, from psychology to economics, are under demand for constant development and refinement. This volume features invited overview papers, as well as original research presented at the Sixth Annual Winemiller Conference: Methodological Developments of Statistics in the Social Sciences, an international meeting that focused on fostering collaboration among mathematical statisticians and social science researchers. The book provides an accessible and insightful look at modern approaches to identifying and describing current, effective methodologies that ultimately add value to various fields of social science research. With contributions from leading international experts on the topic, the book features in-depth coverage of modern quantitative social sciences topics, including: Correlation Structures Structural Equation Models and Recent Extensions Order-Constrained Proximity Matrix Representations Multi-objective and Multi-dimensional Scaling Differences in Bayesian and Non-Bayesian Inference Bootstrap Test of Shape Invariance across Distributions Statistical Software for the Social Sciences Statistics in the Social Sciences: Current Methodological Developments is an excellent supplement for graduate courses on social science statistics in both statistics departments and quantitative social sciences programs. It is also a valuable reference for researchers and practitioners in the fields of psychology, sociology, economics, and market research.

Mathematical models have become a crucial way for the Earth scientist to understand and predict how our planet functions and evolves through time and space. The finite element method (FEM) is a remarkably flexible and powerful tool with enormous potential in the Earth Sciences. This pragmatic guide explores how a variety of different Earth science problems can be translated and solved with FEM, assuming only basic programming experience. This book begins with a general introduction to numerical modeling and includes multiple sample Matlab codes to illustrate how FEM is implemented in practice. Textboxes have been included to provide additional detail, such as specialized Matlab usage or advanced topics. Covering all the key aspects, this is essential reading for those looking to master the technique, as well as those simply seeking to increase their basic level of understanding and appreciation of FEM.

This title focuses on the evolution of the modeling process and on new research perspectives in theoretical and applied geography, as well as spatial planning. In the last 50 years, the achievements of spatial analysis models opened the way to a new understanding of the relationship between society and geographical space. In this book, these models are confronted by the real conditions of territorial prospect, regional dynamism, cultural policy, HMO, and spatial segregation. This confrontation takes into account the instability of social behavior and the permanence of partial determinist trajectories.

Presents new models, methods, and techniques and considers important real-world applications in political science, sociology, economics, marketing, and finance Emphasizing interdisciplinary coverage, Bayesian Inference in the Social Sciences builds upon the recent growth in Bayesian methodology and examines an array of topics in model formulation, estimation, and applications. The book presents recent and trending developments in a diverse, yet closely integrated, set of research topics within the social sciences and facilitates the transmission of new ideas and methodology across disciplines while maintaining manageability, coherence, and a clear focus. Bayesian Inference in the Social Sciences features innovative methodology and novel applications in addition to new theoretical developments and modeling approaches, including the formulation and analysis of models with partial observability, sample selection, and incomplete data. Additional areas of inquiry include a Bayesian derivation of empirical likelihood and method of moment estimators, and the analysis of treatment effect models with endogeneity. The book emphasizes practical implementation, reviews and extends estimation algorithms, and examines innovative applications in a multitude of fields. Time series techniques and algorithms are discussed for stochastic volatility, dynamic factor, and time-varying parameter models. Additional features include: Real-world applications and case studies that highlight asset pricing under fat-tailed distributions, price indifference modeling and market segmentation, analysis of dynamic networks, ethnic minorities and civil war, school choice effects, and business cycles and macroeconomic performance State-of-the-art computational tools and Markov chain Monte Carlo algorithms with related materials available via the book’s supplemental website Interdisciplinary coverage from well-known international scholars and practitioners Bayesian Inference in the Social Sciences is an ideal reference for researchers in economics, political science, sociology, and business as well as an excellent resource for academic, government, and regulation agencies. The book is also useful for graduate-level courses in applied econometrics, statistics, mathematical modeling and simulation, numerical methods, computational analysis, and the social sciences.

The Most Comprehensive and Cutting-Edge Guide to Statistical Applications in Biomedical Research With the increasing use of biotechnology in medical research and the sophisticated advances in computing, it has become essential for practitioners in the biomedical sciences to be fully educated on the role statistics plays in ensuring the accurate analysis of research findings. Statistical Advances in the Biomedical Sciences explores the growing value of statistical knowledge in the management and comprehension of medical research and, more specifically, provides an accessible introduction to the contemporary methodologies used to understand complex problems in the four major areas of modern-day biomedical science: clinical trials, epidemiology, survival analysis, and bioinformatics. Composed of contributions from eminent researchers in the field, this volume discusses the application of statistical techniques to various aspects of modern medical research and illustrates how these methods ultimately prove to be an indispensable part of proper data collection and analysis. A structural uniformity is maintained across all chapters, each beginning with an introduction that discusses general concepts and the biomedical problem under focus and is followed by specific details on the associated methods, algorithms, and applications. In addition, each chapter provides a summary of the main ideas and offers a concluding remarks section that presents novel ideas, approaches, and challenges for future research. Complete with detailed references and insight on the future directions of biomedical research, Statistical Advances in the Biomedical Sciences provides vital statistical guidance to practitioners in the biomedical sciences while also introducing statisticians to new, multidisciplinary frontiers of application. This text is an excellent reference for graduate- and PhD-level courses in various areas of biostatistics and the medical sciences and also serves as a valuable tool for medical researchers, statisticians, public health professionals, and biostatisticians.