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Line Integral Methods for Conservative Problems

Line Integral Methods for Conservative Problems

Line Integral Methods for Conservative Problems explains the numerical solution of differential equations within the framework of geometric integration a branch of numerical analysis that devises numerical methods able to reproduce (in the discrete solution) relevant geometric properties of the continuous vector field. The book focuses on a large set of differential systems named conservative problems particularly Hamiltonian systems. Assuming only basic knowledge of numerical quadrature and Runge–Kutta methods this self-contained book begins with an introduction to the line integral methods. It describes numerous Hamiltonian problems encountered in a variety of applications and presents theoretical results concerning the main instance of line integral methods: the energy-conserving Runge–Kutta methods also known as Hamiltonian boundary value methods (HBVMs). The authors go on to address the implementation of HBVMs in order to recover in the numerical solution what was expected from the theory. The book also covers the application of HBVMs to handle the numerical solution of Hamiltonian partial differential equations (PDEs) and explores extensions of the energy-conserving methods. With many examples of applications this book provides an accessible guide to the subject yet gives you enough details to allow concrete use of the methods. MATLAB codes for implementing the methods are available online.

GBP 59.99
1

Pricing in General Insurance

Pricing in General Insurance

Based on the syllabus of the actuarial profession courses on general insurance pricing – with additional material inspired by the author’s own experience as a practitioner and lecturer – Pricing in General Insurance Second Edition presents pricing as a formalised process that starts with collecting information about a particular policyholder or risk and ends with a commercially informed rate. The first edition of the book proved very popular among students and practitioners with its pragmatic approach informal style and wide-ranging selection of topics including: Background and context for pricing Process of experience rating ranging from traditional approaches (burning cost analysis) to more modern approaches (stochastic modelling) Exposure rating for both property and casualty products Specialised techniques for personal lines (e. g. GLMs) reinsurance and specific products such as credit risk and weather derivatives General-purpose techniques such as credibility multi-line pricing and insurance optimisation The second edition is a substantial update on the first edition including: New chapter on pricing models: their structure development calibration and maintenance New chapter on rate change calculations and the pricing cycle Substantially enhanced treatment of exposure rating increased limit factors burning cost analysis Expanded treatment of triangle-free techniques for claim count development Improved treatment of premium building and capital allocation Expanded treatment of machine learning Enriched treatment of rating factor selection and the inclusion of generalised additive models The book delivers a practical introduction to all aspects of general insurance pricing and is aimed at students of general insurance and actuarial science as well as practitioners in the field. It is complemented by online material such as spreadsheets which implement the techniques described in the book solutions to problems a glossary and other appendices – increasing the practical value of the book.

GBP 74.99
1

Advances in Distance Learning in Times of Pandemic

Advances in Distance Learning in Times of Pandemic

The book Advances in Distance Learning in Times of Pandemic is devoted to the issues and challenges faced by universities in the field of distance learning in COVID-19 times. It covers both the theoretical and practical aspects connected to distance education. It elaborates on issues regarding distance learning its challenges assessment by students and their expectations the use of tools to improve distance learning and the functioning of e-learning in the industry 4. 0 and society 5. 0 eras. The book also devotes a lot of space to the issues of Web 3. 0 in university e-learning quality assurance and knowledge management. The aim and scope of this book is to draw a holistic picture of ongoing online teaching-activities before and during the lockdown period and present the meaning and future of e-learning from students’ points of view taking into consideration their attitudes and expectations as well as industry 4. 0 and society 5. 0 aspects. The book presents the approach to distance learning and how it has changed especially during a pandemic that revolutionized education. It highlights • the function of online education and how that has changed before and during the pandemic. • how e-learning is beneficial in promoting digital citizenship. • distance learning characteristic in the era of industry 4. 0 and society 5. 0. • how the era of industry 4. 0 treats distance learning as a desirable form of education. The book covers both scientific and educational aspects and can be useful for university-level undergraduate postgraduate and research-grade courses and can be referred to by anyone interested in exploring the diverse aspects of distance learning.

GBP 110.00
1

Correspondence Analysis in Practice

Correspondence Analysis in Practice

Drawing on the author’s 45 years of experience in multivariate analysis Correspondence Analysis in Practice Third Edition shows how the versatile method of correspondence analysis (CA) can be used for data visualization in a wide variety of situations. CA and its variants subset CA multiple CA and joint CA translate two-way and multi-way tables into more readable graphical forms — ideal for applications in the social environmental and health sciences as well as marketing economics linguistics archaeology and more. Michael Greenacre is Professor of Statistics at the Universitat Pompeu Fabra Barcelona Spain where he teaches a course amongst others on Data Visualization. He has authored and co-edited nine books and 80 journal articles and book chapters mostly on correspondence analysis the latest being Visualization and Verbalization of Data in 2015. He has given short courses in fifteen countries to environmental scientists sociologists data scientists and marketing professionals and has specialized in statistics in ecology and social science.

GBP 48.99
1

Statistical Models in Toxicology

Statistical Models in Toxicology

Statistical Models in Toxicology presents an up-to-date and comprehensive account of statistical theory topics that occur in toxicology. The attention given by statisticians to the problem of health risk estimation for environmental and occupational exposures in the last few decades has created excitement and optimism among both statisticians and toxicologists. The development of modern statistical techniques with solid mathematical foundations in the twentieth century and the advent of modern computers in the latter part of the century gave way to the development of many statistical models and methods to describe toxicological processes and attempts to solve the associated problems. Not only have the models enjoyed a high level of elegance and sophistication mathematically but they are widely used by industry and government regulatory agencies. Features:Focuses on describing the statistical models in environmental toxicology that facilitate the assessment of risk mainly in humans. The properties and shortfalls of each model are discussed and its impact in the process of risk assessment is examined. Discusses models that assess the risk of mixtures of chemicals. Presents statistical models that are developed for risk estimation in different aspects of environmental toxicology including cancer and carcinogenic substances. Includes models for developmental and reproductive toxicity risk assessment risk assessment in continuous outcomes and developmental neurotoxicity. Contains numerous examples and exercises. Statistical Models in Toxicology introduces a wide variety of statistical models that are currently utilized for dose-response modeling and risk analysis. These models are often developed based on design and regulatory guidelines of toxicological experiments. The book is suitable for practitioners or it can be used as a textbook for advanced undergraduate or graduate students of mathematics and statistics.

GBP 44.99
1

Visualizing Surveys in R

Visualizing Surveys in R

Visualizing Surveys in R is about creating static print quality figures from survey data using R. The focus is not for example on statistical analysis of survey data but rather on giving concrete solutions for typical problems in visualizing survey data. While there are many excellent books on data visualization surveys and R the aim of this book is to bring these topics together and offer practical instructions for visualizing surveys in R. Features Introduction to survey data: variables categories and scales Description of a process for visualizing survey data Recommendations for reading survey data into R Advice on building a survey dataset in R to facilitate versatile plotting Step-by-step recipes in R for creating useful plots from survey data The book is intended for researchers who regularly use surveys and are interested in learning how to seize the vast possibilities and the flexibility of R in survey analysis and visualizations. The book is also valuable for psychologists marketeers HR personnel managers and other professionals who wish to standardize and automate the process for visualizing survey data. Finally the book is suitable as a course textbook either more widely on survey studies or more strictly on visualizing survey data in R.

GBP 140.00
1

Exercises in Programming Style

Exercises in Programming Style

The first edition of Exercises in Programming Style was honored as an ACM Notable Book and praised as The best programming book of the decade. This new edition retains the same presentation but has been upgraded to Python 3 and there is a new section on neural network styles. Using a simple computational task (term frequency) to illustrate different programming styles Exercises in Programming Style helps readers understand the various ways of writing programs and designing systems. It is designed to be used in conjunction with code provided on an online repository. The book complements and explains the raw code in a way that is accessible to anyone who regularly practices the art of programming. The book can also be used in advanced programming courses in computer science and software engineering programs. The book contains 40 different styles for writing the term frequency task. The styles are grouped into ten categories: historical basic function composition objects and object interactions reflection and metaprogramming adversity data-centric concurrency interactivity and neural networks. The author states the constraints in each style and explains the example programs. Each chapter first presents the constraints of the style next shows an example program and then gives a detailed explanation of the code. Most chapters also have sections focusing on the use of the style in systems design as well as sections describing the historical context in which the programming style emerged.

GBP 35.99
1

Tree-Based Methods for Statistical Learning in R

Tree-Based Methods for Statistical Learning in R

Tree-based Methods for Statistical Learning in R provides a thorough introduction to both individual decision tree algorithms (Part I) and ensembles thereof (Part II). Part I of the book brings several different tree algorithms into focus both conventional and contemporary. Building a strong foundation for how individual decision trees work will help readers better understand tree-based ensembles at a deeper level which lie at the cutting edge of modern statistical and machine learning methodology. The book follows up most ideas and mathematical concepts with code-based examples in the R statistical language; with an emphasis on using as few external packages as possible. For example users will be exposed to writing their own random forest and gradient tree boosting functions using simple for loops and basic tree fitting software (like rpart and party/partykit) and more. The core chapters also end with a detailed section on relevant software in both R and other opensource alternatives (e. g. Python Spark and Julia) and example usage on real data sets. While the book mostly uses R it is meant to be equally accessible and useful to non-R programmers. Consumers of this book will have gained a solid foundation (and appreciation) for tree-based methods and how they can be used to solve practical problems and challenges data scientists often face in applied work. Features: Thorough coverage from the ground up of tree-based methods (e. g. CART conditional inference trees bagging boosting and random forests). A companion website containing additional supplementary material and the code to reproduce every example and figure in the book. A companion R package called treemisc which contains several data sets and functions used throughout the book (e. g. there’s an implementation of gradient tree boosting with LAD loss that shows how to perform the line search step by updating the terminal node estimates of a fitted rpart tree). Interesting examples that are of practical use; for example how to construct partial dependence plots from a fitted model in Spark MLlib (using only Spark operations) or post-processing tree ensembles via the LASSO to reduce the number of trees while maintaining or even improving performance.

GBP 82.99
1

Statistics in Engineering With Examples in MATLAB and R Second Edition

Statistics in Engineering With Examples in MATLAB and R Second Edition

Engineers are expected to design structures and machines that can operate in challenging and volatile environments while allowing for variation in materials and noise in measurements and signals. Statistics in Engineering Second Edition: With Examples in MATLAB and R covers the fundamentals of probability and statistics and explains how to use these basic techniques to estimate and model random variation in the context of engineering analysis and design in all types of environments. The first eight chapters cover probability and probability distributions graphical displays of data and descriptive statistics combinations of random variables and propagation of error statistical inference bivariate distributions and correlation linear regression on a single predictor variable and the measurement error model. This leads to chapters including multiple regression; comparisons of several means and split-plot designs together with analysis of variance; probability models; and sampling strategies. Distinctive features include: All examples based on work in industry consulting to industry and research for industry Examples and case studies include all engineering disciplinesEmphasis on probabilistic modeling including decision trees Markov chains and processes and structure functionsIntuitive explanations are followed by succinct mathematical justificationsEmphasis on random number generation that is used for stochastic simulations of engineering systems demonstration of key concepts and implementation of bootstrap methods for inferenceUse of MATLAB and the open source software R both of which have an extensive range of statistical functions for standard analyses and also enable programing of specific applicationsUse of multiple regression for times series models and analysis of factorial and central composite designs Inclusion of topics such as Weibull analysis of failure times and split-plot designs that are commonly used in industry but are not usually included in introductory textbooksExperiments designed to show fundamental concepts that have been tested with large classes working in small groupsWebsite with additional materials that is regularly updatedAndrew Metcalfe David Green Andrew Smith and Jonathan Tuke have taught probability and statistics to students of engineering at the University of Adelaide for many years and have substantial industry experience. Their current research includes applications to water resources engineering mining and telecommunications. Mahayaudin Mansor worked in banking and insurance before teaching statistics and business mathematics at the Universiti Tun Abdul Razak Malaysia and is currently a researcher specializing in data analytics and quantitative research in the Health Economics and Social Policy Research Group at the Australian Centre for Precision Health University of South Australia. Tony Greenfield formerly Head of Process Computing and Statistics at the British Iron and Steel Research Association is a statistical consultant. He has been awarded the Chambers Medal for outstanding services to the Royal Statistical Society; the George Box Medal by the European Network for Business and Industrial Statistics for Outstanding Contributions to Industrial Statistics; and the William G. Hunter Award by the American Society for | Statistics in Engineering With Examples in MATLAB® and R Second Edition

GBP 44.99
1

Mathematics in Cyber Research

Mathematics in Cyber Research

In the last decade both scholars and practitioners have sought novel ways to address the problem of cybersecurity. Innovative outcomes have included applications such as blockchain as well as creative methods for cyber forensics software development and intrusion prevention. Accompanying these technological advancements discussion on cyber matters at national and international levels has focused primarily on the topics of law policy and strategy. The objective of these efforts is typically to promote security by establishing agreements among stakeholders on regulatory activities. Varying levels of investment in cyberspace however comes with varying levels of risk; in some ways this can translate directly to the degree of emphasis for pushing substantial change. At the very foundation or root of cyberspace systems and processes are tenets and rules governed by principles in mathematics. Topics such as encrypting or decrypting file transmissions modeling networks performing data analysis quantifying uncertainty measuring risk and weighing decisions or adversarial courses of action represent a very small subset of activities highlighted by mathematics. To facilitate education and a greater awareness of the role of mathematics in cyber systems and processes a description of research in this area is needed. Mathematics in Cyber Research aims to familiarize educators and young researchers with the breadth of mathematics in cyber-related research. Each chapter introduces a mathematical sub-field describes relevant work in this field associated with the cyber domain provides methods and tools as well as details cyber research examples or case studies. Features One of the only books to bring together such a diverse and comprehensive range of topics within mathematics and apply them to cyber research. Suitable for college undergraduate students or educators that are either interested in learning about cyber-related mathematics or intend to perform research within the cyber domain. The book may also appeal to practitioners within the commercial or government industry sectors. Most national and international venues for collaboration and discussion on cyber matters have focused primarily on the topics of law policy strategy and technology. This book is among the first to address the underpinning mathematics.

GBP 160.00
1

Deep Learning in Practice

Deep Learning in Practice

Deep Learning in Practice helps you learn how to develop and optimize a model for your projects using Deep Learning (DL) methods and architectures. Key features: Demonstrates a quick review on Python NumPy and TensorFlow fundamentals. Explains and provides examples of deploying TensorFlow and Keras in several projects. Explains the fundamentals of Artificial Neural Networks (ANNs). Presents several examples and applications of ANNs. Learning the most popular DL algorithms features. Explains and provides examples for the DL algorithms that are presented in this book. Analyzes the DL network’s parameter and hyperparameters. Reviews state-of-the-art DL examples. Necessary and main steps for DL modeling. Implements a Virtual Assistant Robot (VAR) using DL methods. Necessary and fundamental information to choose a proper DL algorithm. Gives instructions to learn how to optimize your DL model IN PRACTICE. This book is useful for undergraduate and graduate students as well as practitioners in industry and academia. It will serve as a useful reference for learning deep learning fundamentals and implementing a deep learning model for any project step by step.

GBP 77.99
1

Polynomial Completeness in Algebraic Systems

Polynomial Completeness in Algebraic Systems

Boolean algebras have historically played a special role in the development of the theory of general or universal algebraic systems providing important links between algebra and analysis set theory mathematical logic and computer science. It is not surprising then that focusing on specific properties of Boolean algebras has lead to new directions in universal algebra. In the first unified study of polynomial completeness Polynomial Completeness in Algebraic Systems focuses on and systematically extends another specific property of Boolean algebras: the property of affine completeness. The authors present full proof that all affine complete varieties are congruence distributive and that they are finitely generated if and only if they can be presented using only a finite number of basic operations. In addition to these important findings the authors describe the different relationships between the properties of lattices of equivalence relations and the systems of functions compatible with them. An introductory chapter surveys the appropriate background material exercises in each chapter allow readers to test their understanding and open problems offer new research possibilities. Thus Polynomial Completeness in Algebraic Systems constitutes an accessible coherent presentation of this rich topic valuable to both researchers and graduate students in general algebraic systems.

GBP 59.99
1

Formal Methods in Computer Science

Formal Methods in Computer Science

Formal Methods in Computer Science gives students a comprehensive introduction to formal methods and their application in software and hardware specification and verification. The first part introduces some fundamentals in formal methods including set theory functions finite state machines and regular expressions. The second part focuses on logic a powerful formal language in specifying systems properties. It covers propositional logic predicate logic temporal logic and model checking. The third part presents Petri nets the most popular formal language in system behavior modeling. In additional to regular Petri nets this part also examines timed Petri nets and high-level Petri nets. The textbook is ideal for undergraduate or graduate courses in computer engineering software engineering computer science and information technology programs. Parts of the book are useful reading material in undergraduate computer course and as a reference guide for students researching the area of formal system specification and validation. Features * Introduces a comprehensive yet manageable set of formal techniques for computer science students * Stresses real-world application value of each formal technique * Offers a good set of exercises which help students better understand the presented techniques * Also offers a prepared source code for downloading and non-commercial use

GBP 69.99
1

Entropy Randomization in Machine Learning

Entropy Randomization in Machine Learning

Entropy Randomization in Machine Learning presents a new approach to machine learning—entropy randomization—to obtain optimal solutions under uncertainty (uncertain data and models of the objects under study). Randomized machine-learning procedures involve models with random parameters and maximum entropy estimates of the probability density functions of the model parameters under balance conditions with measured data. Optimality conditions are derived in the form of nonlinear equations with integral components. A new numerical random search method is developed for solving these equations in a probabilistic sense. Along with the theoretical foundations of randomized machine learning Entropy Randomization in Machine Learning considers several applications to binary classification modelling the dynamics of the Earth’s population predicting seasonal electric load fluctuations of power supply systems and forecasting the thermokarst lakes area in Western Siberia. Features • A systematic presentation of the randomized machine-learning problem: from data processing through structuring randomized models and algorithmic procedure to the solution of applications-relevant problems in different fields • Provides new numerical methods for random global optimization and computation of multidimensional integrals • A universal algorithm for randomized machine learning This book will appeal to undergraduates and postgraduates specializing in artificial intelligence and machine learning researchers and engineers involved in the development of applied machine learning systems and researchers of forecasting problems in various fields.

GBP 82.99
1

Data Science in Practice

Data Science in Practice

Data Science in Practice is the ideal introduction to data science. With or without math skills here you get the all-round view that you need for your projects. This book describes how to properly question data in order to unearth the treasure that data can be. You will get to know the relevant analysis methods and will be introduced to the programming language R which is ideally suited for data analysis. Associated tools like notebooks that make data science programming easily accessible are included in this introduction. Because technology alone is not enough this book also deals with problems in project implementation illuminates various fields of application and does not forget to address ethical aspects. Data Science in Practice includes many examples notes on errors decision-making aids and other practical tips. This book is ideal as a complementary text for university students and is a useful learning tool for those moving into more data-related roles. Key Features: Success factors and tools for all project phases Includes application examples for various subject areas Introduces many aspects of Data Science from requirements analysis to data acquisition and visualization

GBP 54.99
1

Statistics in MATLAB A Primer

Statistics in MATLAB A Primer

Fulfilling the need for a practical user's guide Statistics in MATLAB: A Primer provides an accessible introduction to the latest version of MATLAB and its extensive functionality for statistics. Assuming a basic knowledge of statistics and probability as well as a fundamental understanding of linear algebra concepts this book covers capabilities in the main MATLAB package and the Statistics Toolbox. The student version of MATLAB presents examples of how MATLAB can be used to analyze data and offers access to a companion website with data sets and additional examples. It contains figures and visual aids to assist in application of the software and explains how to determine what method should be used for analysis. Statistics in MATLAB: A Primer is an ideal reference for undergraduate and graduate students in engineering mathematics statistics economics biostatistics and computer science. It is also appropriate for a diverse professional market making it a valuable addition to the libraries of researchers in statistics computer science data mining machine learning image analysis signal processing and engineering. | Statistics in MATLAB A Primer

GBP 175.00
1

Kinetic Modelling in Systems Biology

Kinetic Modelling in Systems Biology

With more and more interest in how components of biological systems interact it is important to understand the various aspects of systems biology. Kinetic Modelling in Systems Biology focuses on one of the main pillars in the future development of systems biology. It explores both the methods and applications of kinetic modeling in this emerging field. The book introduces the basic biological cellular network concepts in the context of cellular functioning explains the main aspects of the Edinburgh Pathway Editor (EPE) software package and discusses the process of constructing and verifying kinetic models. It presents the features user interface and examples of DBSolve as well as the principles of modeling individual enzymes and transporters. The authors describe how to construct kinetic models of intracellular systems on the basis of models of individual enzymes. They also illustrate how to apply the principles of kinetic modeling to collect all available information on the energy metabolism of whole organelles construct a kinetic model and predict the response of the organelle to changes in external conditions. The final chapter focuses on applications of kinetic modeling in biotechnology and biomedicine. Encouraging readers to think about future challenges this book will help them understand the kinetic modeling approach and how to apply it to solve real-life problems. Downloadable Resources FeaturesExtensively used throughout the text for pathway visualization and illustration the EPE software is available on the accompanying downloadable resources. The downloadable resources also include pathway diagrams in several graphical formats DBSolve installation with examples and all models from the book with dynamic visualization of simulation results allowing readers to perform in silico simulations and use the models as templates for further applications.

GBP 56.99
1

Topics in Graph Theory

Topics in Graph Theory

The interplay continues to grow between graph theory and a wide variety of models and applications in mathematics computer science operations research and the natural and social sciences. Topics in Graph Theory is geared toward the more mathematically mature student. The first three chapters provide the basic definitions and theorems of graph theory and the remaining chapters introduce a variety of topics and directions for research. These topics draw on numerous areas of theoretical and applied mathematics including combinatorics probability linear algebra group theory topology operations research and computer science. This makes the book appropriate for a first course at the graduate level or as a second course at the undergraduate level. The authors build upon material previously published in Graph Theory and Its Applications Third Edition by the same authors. That text covers material for both an undergraduate and graduate course while this book builds on and expands the graduate-level material. Features Extensive exercises and applications. Flexibility: appropriate for either a first course at the graduate level or an advanced course at the undergraduate level. Opens avenues to a variety of research areas in graph theory. Emphasis on topological and algebraic graph theory.

GBP 74.99
1

Statistics in Toxicology Using R

Statistics in Toxicology Using R

The apparent contradiction between statistical significance and biological relevance has diminished the value of statistical methods as a whole in toxicology. Moreover recommendations for statistical analysis are imprecise in most toxicological guidelines. Addressing these dilemmas Statistics in Toxicology Using R explains the statistical analysis of selected experimental data in toxicology and presents assay-specific suggestions such as for the in vitro micronucleus assay. Mostly focusing on hypothesis testing the book covers standardized bioassays for chemicals drugs and environmental pollutants. It is organized according to selected toxicological assays including: Short-term repeated toxicity studiesLong-term carcinogenicity assays Studies on reproductive toxicity Mutagenicity assays Toxicokinetic studies The book also discusses proof of safety (particularly in ecotoxicological assays) toxicogenomics the analysis of interlaboratory studies and the modeling of dose-response relationships for risk assessment. For each toxicological problem the author describes the statistics involved matching data example R code and outcomes and their interpretation. This approach allows you to select a certain bioassay identify the specific data structure run the R code with the data example understand the test outcome and interpretation and replace the data set with your own data and run again.

GBP 44.99
1

Bayesian Methods in Pharmaceutical Research

Bayesian Methods in Pharmaceutical Research

Since the early 2000s there has been increasing interest within the pharmaceutical industry in the application of Bayesian methods at various stages of the research development manufacturing and health economic evaluation of new health care interventions. In 2010 the first Applied Bayesian Biostatistics conference was held with the primary objective to stimulate the practical implementation of Bayesian statistics and to promote the added-value for accelerating the discovery and the delivery of new cures to patients. This book is a synthesis of the conferences and debates providing an overview of Bayesian methods applied to nearly all stages of research and development from early discovery to portfolio management. It highlights the value associated with sharing a vision with the regulatory authorities academia and pharmaceutical industry with a view to setting up a common strategy for the appropriate use of Bayesian statistics for the benefit of patients. The book covers:Theory methods applications and computingBayesian biostatistics for clinical innovative designsAdding value with Real World EvidenceOpportunities for rare orphan diseases and pediatric developmentApplied Bayesian biostatistics in manufacturingDecision making and Portfolio management Regulatory perspective and public health policies Statisticians and data scientists involved in the research development and approval of new cures will be inspired by the possible applications of Bayesian methods covered in the book. The methods applications and computational guidance will enable the reader to apply Bayesian methods in their own pharmaceutical research. | Bayesian Methods in Pharmaceutical Research

GBP 44.99
1

Knowledge Discovery in Proteomics

Knowledge Discovery in Proteomics

Multi-modal representations the lack of complete and consistent domain theories rapid evolution of domain knowledge high dimensionality and large amounts of missing information - these are challenges inherent in modern proteomics. As our understanding of protein structure and function becomes ever more complicated we have reached a point where the actual management of data is a major stumbling block to the interpretation of results from proteomic platforms to knowledge discovery. Knowledge Discovery in Proteomics presents timely authoritative discussions on some of the key issues in high-throughput proteomics exploring examples that represent some of the major challenges of knowledge discovery in the field. The authors focus on five specific domains:Mass spectrometry-based protein analysisProtein-protein interaction network analysisSystematic high-throughput protein crystallizationSystematic integrated analysis of multiple data repositoriesSystems biologyIn each area the authors describe the challenges created by the type of data produced and present potential solutions to the problem of data mining within the domain. They take a systems approach covering individual data and integrating its computational aspects from data preprocessing storage and access to analysis visualization and interpretation. With clear exposition practical examples and rich illustrations this book presents an outstanding overview of this emerging field and builds the background needed for the fruitful exchange of ideas between computational and biological scientists.

GBP 59.99
1

Bioequivalence and Statistics in Clinical Pharmacology

Bioequivalence and Statistics in Clinical Pharmacology

Maintaining a practical perspective Bioequivalence and Statistics in Clinical Pharmacology Second Edition explores statistics used in day-to-day clinical pharmacology work. The book is a starting point for those involved in such research and covers the methods needed to design analyze and interpret bioequivalence trials; explores when how and why these studies are performed as part of drug development; and demonstrates the methods using real world examples. Drawing on knowledge gained directly from working in the pharmaceutical industry the authors set the stage by describing the general role of statistics. Once the foundation of clinical pharmacology drug development regulatory applications and the design and analysis of bioequivalence trials are established including recent regulatory changes in design and analysis and in particular sample-size adaptation they move on to related topics in clinical pharmacology involving the use of cross-over designs. These include but are not limited to safety studies in Phase I dose-response trials drug interaction trials food-effect and combination trials QTc and other pharmacodynamic equivalence trials proof-of-concept trials dose-proportionality trials and vaccines trials. This second edition addresses several recent developments in the field including new chapters on adaptive bioequivalence studies scaled average bioequivalence testing and vaccine trials. Purposefully designed to be instantly applicable Bioequivalence and Statistics in Clinical Pharmacology Second Edition provides examples of SAS and R code so that the analyses described can be immediately implemented. The authors have made extensive use of the proc mixed procedures available in SAS.

GBP 44.99
1

Acceptance Sampling in Quality Control

Acceptance Sampling in Quality Control

Acceptance Sampling in Quality Control Third Edition presents the state of the art in the methodology of sampling while integrating both theory and best practices. It discusses various standards including those from the ISO MIL-STD and ASTM and explores how to set quality levels. The book also includes problems at the end of each chapter with solutions. This edition improves upon the previous editions especially in the areas of software applications and compliance sampling plans. New to the Third Edition: Numerous Microsoft Excel templates to address sampling plans are used. Commercial software applications are discussed at the end of many chapters. Discussion of quick switching systems has been expanded to account for the considerable recent activity in this area. Added discussion of zero acceptance number chained quick switching systems.

GBP 115.00
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