Applications Of Monte Carlo Methods In Biology Medicine And Other Fields Of Science

This volume is an eclectic mix of applications of Monte Carlo methods in many fields of research should not be surprising, because of the ubiquitous use of these methods in many fields of human endeavor. In an attempt to focus attention on a manageable set of applications, the main thrust of this book is to emphasize applications of Monte Carlo simulation methods in biology and medicine.

Modern computer-intensive statistical methods play a key role in solving many problems across a wide range of scientific disciplines. This new edition of the bestselling Randomization, Bootstrap and Monte Carlo Methods in Biology illustrates the value of a number of these methods with an emphasis on biological applications. This textbook focuses on three related areas in computational statistics: randomization, bootstrapping, and Monte Carlo methods of inference. The author emphasizes the sampling approach within randomization testing and confidence intervals. Similar to randomization, the book shows how bootstrapping, or resampling, can be used for confidence intervals and tests of significance. It also explores how to use Monte Carlo methods to test hypotheses and construct confidence intervals. New to the Third Edition Updated information on regression and time series analysis, multivariate methods, survival and growth data as well as software for computational statistics References that reflect recent developments in methodology and computing techniques Additional references on new applications of computer-intensive methods in biology Providing comprehensive coverage of computer-intensive applications while also offering data sets online, Randomization, Bootstrap and Monte Carlo Methods in Biology, Third Edition supplies a solid foundation for the ever-expanding field of statistics and quantitative analysis in biology.

Annual Reports in Computational Chemistry provides timely and critical reviews of important topics in computational chemistry as applied to all chemical disciplines. Topics covered include quantum chemistry, molecular mechanics, force fields, chemical education, and applications in academic and industrial settings. Focusing on the most recent literature and advances in the field, each article covers a specific topic of importance to computational chemists. Quantum chemistry Molecular mechanics Force fields Chemical education and applications in academic and industrial settings

The Monte Carlo method is a numerical technique to model the probability of all possible outcomes in a process that cannot easily be predicted due to the interference of random variables. It is a technique used to understand the impact of risk, uncertainty, and ambiguity in forecasting models. However, this technique is complicated by the amount of computer time required to achieve sufficient precision in the simulations and evaluate their accuracy. This book discusses the general principles of the Monte Carlo method with an emphasis on techniques to decrease simulation time and increase accuracy.

This book presents a consistent methodology for making decisions under uncertain conditions, as is almost always the case. Tools such as value of information and value of flexibility are explored as a means to make more complex and nuanced decisions. The book develops the complete formalism for assessing the value of acquiring information with two novel approaches. Firstly, it integrates the fuzzy characteristics of data, and secondly develops a methodology for assessing data acquisition actions that optimize the value of projects from a holistic perspective. The book also discusses the formalism for including flexibility in the project decision assessment. Practical examples of oil- and gas-related decision problems are included and discussed to facilitate the learning process. This book provides valuable advice and case studies applicable to engineers, researchers, and graduate students, particularly in the oil and gas industry and pharmaceutic industry.

From first principles to current computer applications, Monte Carlo Calculations in Nuclear Medicine, Second Edition: Applications in Diagnostic Imaging covers the applications of Monte Carlo calculations in nuclear medicine and critically reviews them from a diagnostic perspective. Like the first edition, this book explains the Monte Carlo method and the principles behind SPECT and PET imaging, introduces the reader to some Monte Carlo software currently in use, and gives the reader a detailed idea of some possible applications of Monte Carlo in current research in SPECT and PET. New chapters in this edition cover codes and applications in pre-clinical PET and SPECT. The book explains how Monte Carlo methods and software packages can be applied to evaluate scatter in SPECT and PET imaging, collimation, and image deterioration. A guide for researchers and students developing methods to improve image resolution, it also demonstrates how Monte Carlo techniques can be used to simulate complex imaging systems.

The purpose of this book is to introduce researchers and practitioners to recent advances and applications of Monte Carlo Simulation (MCS). Random sampling is the key of the MCS technique. The 11 chapters of this book collectively illustrates how such a sampling technique is exploited to solve difficult problems or analyze complex systems in various engineering and science domains. Issues related to the use of MCS including goodness-of-fit, uncertainty evaluation, variance reduction, optimization, and statistical estimation are discussed and examples of solutions are given. Novel applications of MCS are demonstrated in financial systems modeling, estimation of transition behavior of organic molecules, chemical reaction, particle diffusion, kinetic simulation of biophysics and biological data, and healthcare practices. To enlarge the accessibility of this book, both field-specific background materials and field-specific usages of MCS are introduced in most chapters. The aim of this book is to unify knowledge of MCS from different fields to facilitate research and new applications of MCS.

The use of small animal models in basic and preclinical sciences constitutes an integral part of testing new pharmaceutical agents prior to their application in clinical practice. New imaging and therapeutic approaches need to be tested and validated first in animals before application to humans. Handbook of Small Animal Imaging: Preclinical Imaging, Therapy, and Applications collects the latest information about various imaging and therapeutic technologies used in preclinical research into a single source. Useful to established researchers as well as newcomers to the field, this handbook shows readers how to exploit and integrate these imaging and treatment modalities and techniques into their own research. The book first presents introductory material on small animal imaging, therapy, and research ethics. It next covers ionizing radiation and nonionizing radiation methods in small animal imaging, hybrid imaging, and imaging agents. The book then addresses therapeutic research platforms and image quantification, explaining how to ensure accurate measurements of high-quality data. It concludes with an overview of many small animal imaging and therapy applications that demonstrate the strength of the techniques in biomedical fields.