Search results for "Bayesian Analysis with Python - Third Edition"
Items we need to order for you
Stochastic Analysis in Discrete and Continuous Settings
This monograph is an introduction to some aspects of stochastic analysis in the framework of normal martingales, in both discrete and continuous time. The text is mostly self-contained, except for Section 5.7 that requires some background in geometry, and should be accessible to graduate students and researchers having already received a basic training in probability. Prereq- sites are mostly limited to a knowledge of measure theory and probability, namely?-algebras,expectations,andconditionalexpectations.Ashortint- duction to stochastic calculus for continuous and jump processes is given in Chapter 2 using normal martingales, whose predictable quadratic variation is the Lebesgue measure. There already exists several books devoted to stochastic analysis for c- tinuous di?usion processes on Gaussian and Wiener spaces, cf. e.g. [51], [63], [65], [72], [83], [84], [92], [128], [134], [143], [146], [147]. The particular f- ture of this text is to simultaneously consider continuous processes and jump processes in the uni?ed framework of normal martingales.
R 1,560.93
Business Case Analysis with R
This tutorial teaches you how to use the statistical programming language R to develop a business case simulation and analysis. It presents a methodology for conducting business case analysis that minimizes decision delay by focusing stakeholders on what matters most and suggests pathways for minimizing the risk in strategic and capital allocation decisions. Business case analysis, often conducted in spreadsheets, exposes decision makers to additional risks that arise just from the use of the spreadsheet environment. R has become one of the most widely used tools for reproducible quantitative analysis, and analysts fluent in this language are in high demand. The R language, traditionally used for statistical analysis, provides a more explicit, flexible, and extensible environment than spreadsheets for conducting business case analysis. The main tutorial follows the case in which a chemical manufacturing company considers constructing a chemical reactor and production facility to bring a new compound to market. There are numerous uncertainties and risks involved, including the possibility that a competitor brings a similar product online. The company must determine the value of making the decision to move forward and where they might prioritize their attention to make a more informed and robust decision. While the example used is a chemical company, the analysis structure it presents can be applied to just about any business decision, from IT projects to new product development to commercial real estate. The supporting tutorials include the perspective of the founder of a professional service firm who wants to grow his business and a member of a strategic planning group in a biomedical device company who wants to know how much to budget in order to refine the quality of information about critical uncertainties that might affect the value of a chosen product development pathway. What You’ll LearnSet up a business case abstraction in an influence diagram to communicate the essence of the problem to other stakeholdersModel the inherent uncertainties in the problem with Monte Carlo simulation using the R languageCommunicate the results graphicallyDraw appropriate insights from the resultsDevelop creative decision strategies for thorough opportunity cost analysisCalculate the value of information on critical uncertainties between competing decision strategies to set the budget for deeper data analysisConstruct appropriate information to satisfy the parameters for the Monte Carlo simulation when little or no empirical data are available Who This Book Is For Financial analysts, data practitioners, and risk/business professionals; also appropriate for graduate level finance, business, or data science students
R 912.00
Everything You Wanted to Know About Data Analysis and Fitting but Were Afraid to Ask
These notes describe how to average and fit numerical data that have been obtained either by simulation or measurement. Following an introduction on how to estimate various average values, they discuss how to determine error bars on those estimates, and how to proceed for combinations of measured values. Techniques for fitting data to a given set of models will be described in the second part of these notes. This primer equips readers to properly derive the results covered, presenting the content in a style suitable for a physics audience. It also includes scripts in python, perl and gnuplot for performing a number of tasks in data analysis and fitting, thereby providing readers with a useful reference guide.
R 1,835.75
Analysis and Probability
Combines analysis and tools from probability, harmonic analysis, operator theory, and engineering (signal/image processing)Interdisciplinary focus with hands-on approach, generous motivation and new pedagogical techniquesNumerous exercises reinforce fundamental concepts and hone computational skillsSeparate sections explain engineering terms to mathematicians and operator theory to engineersFills a gap in the literature
R 1,560.32
Statistical Data Analysis for the Physical Sciences
Data analysis lies at the heart of every experimental science. Providing a modern introduction to statistics, this book is ideal for undergraduates in physics. It introduces the necessary tools required to analyse data from experiments across a range of areas, making it a valuable resource for students. In addition to covering the basic topics, the book also takes in advanced and modern subjects, such as neural networks, decision trees, fitting techniques and issues concerning limit or interval setting. Worked examples and case studies illustrate the techniques presented, and end-of-chapter exercises help test the reader's understanding of the material.
R 3,091.15
Multilevel Analysis
Applauded for its clarity, this accessible introduction helps readers apply multilevel techniques to their research. The book also includes advanced extensions, making it useful as both an introduction for students and as a reference for researchers. Basic models and examples are discussed in nontechnical terms with an emphasis on understanding the methodological and statistical issues involved in using these models. The estimation and interpretation of multilevel models is demonstrated using realistic examples from various disciplines including psychology, education, public health, and sociology. Readers are introduced to a general framework on multilevel modeling which covers both observed and latent variables in the same model, while most other books focus on observed variables. In addition, Bayesian estimation is introduced and applied using accessible software.
R 2,613.86
Discrete Choice Analysis with R
This book is designed as a gentle introduction to the fascinating field of choice modeling and its practical implementation using the R language. Discrete choice analysis is a family of methods useful to study individual decision-making. With strong theoretical foundations in consumer behavior, discrete choice models are used in the analysis of health policy, transportation systems, marketing, economics, public policy, political science, urban planning, and criminology, to mention just a few fields of application. The book does not assume prior knowledge of discrete choice analysis or R, but instead strives to introduce both in an intuitive way, starting from simple concepts and progressing to more sophisticated ideas. Loaded with a wealth of examples and code, the book covers the fundamentals of data and analysis in a progressive way. Readers begin with simple data operations and the underlying theory of choice analysis and conclude by working with sophisticated models including latent class logit models, mixed logit models, and ordinal logit models with taste heterogeneity. Data visualization is emphasized to explore both the input data as well as the results of models. This book should be of interest to graduate students, faculty, and researchers conducting empirical work using individual level choice data who are approaching the field of discrete choice analysis for the first time. In addition, it should interest more advanced modelers wishing to learn about the potential of R for discrete choice analysis. By embedding the treatment of choice modeling within the R ecosystem, readers benefit from learning about the larger R family of packages for data exploration, analysis, and visualization.
R 6,948.05
Method Validation in Pharmaceutical Analysis
New edition of the gold standard in the field of pharmaceutical analysis, extensively updated to include the new ICH Guidelines Q2 and Q14 Following an all-encompassing lifecycle approach to analytical procedures in pharmaceutical analysis, Method Validation in Pharmaceutical Analysis provides hands-on information for readers involved in development, validation, and continued maintenance and evaluation of analytical procedures in pharmaceutical analysis. This newly revised and updated Third Edition includes much-needed interpretation of the most recent ICH guidelines for validation and method development, as well as recent publications of the USP Validation & Verification Expert Panel on Analytical Procedure Lifecycle Management and the activities of the British Pharmacopeia AQbD Working Party. It also addresses trending topics in the field such as data integrity and continuous monitoring of analytical performance. Written by a team of highly qualified pharmaceutical professionals, Method Validation in Pharmaceutical Analysis includes information on sample topics such as: Data governance, data integrity, and data quality, as well as analytical instrument qualification and system validation lifecycleContinued HPLC performance qualification, analytical target profile, decision rules and fitness for intended use, and performance characteristics of analytical proceduresMethod selection, development, and optimization, multivariate analytical procedures, and risk assessment and analytical control strategyImplementation of compendial/pharmacopeia test procedures, transfer of analytical procedures, and the lifecycle approach to transfer of analytical procedures Completely comprehensive in coverage, Method Validation in Pharmaceutical Analysis is an essential reference for scientists, researchers, and professionals in the pharmaceutical industry, analytical chemists, QA officers, and public authorities tasked with relevant regulatory responsibilities.
R 6,285.00
Differential Analysis on Complex Manifolds
A brand new appendix by Oscar Garcia-Prada graces this third edition of a classic work. In developing the tools necessary for the study of complex manifolds, this comprehensive, well-organized treatment presents in its opening chapters a detailed survey of recent progress in four areas: geometry (manifolds with vector bundles), algebraic topology, differential geometry, and partial differential equations. Wells’s superb analysis also gives details of the Hodge-Riemann bilinear relations on Kahler manifolds, Griffiths's period mapping, quadratic transformations, and Kodaira's vanishing and embedding theorems. Oscar Garcia-Prada’s appendix gives an overview of the developments in the field during the decades since the book appeared.
R 1,694.01
R 1,099.00
Primer of Genetic Analysis
An invaluable student-tested study aid, this primer, first published in 2007, provides guided instruction for the analysis and interpretation of genetic principles and practice in problem solving. Each section is introduced with a summary of useful hints for problem solving and an overview of the topic with key terms. A series of problems, generally progressing from simple to more complex, then allows students to test their understanding of the material. Each question and answer is accompanied by detailed explanation. This third edition includes additional problems in basic areas that often challenge students, extended coverage in molecular biology and development, an expanded glossary of terms, and updated historical landmarks. Students at all levels, from beginning biologists and premedical students to graduates seeking a review of basic genetics, will find this book a valuable aid. It will complement the formal presentation in any genetics textbook or stand alone as a self-paced review manual.
R 5,289.02
Matrix Analysis Of Framed Structures
Intended for structural engineers, this third edition text, a source on the use of matrix analysis for structures of all types, has been revised and updated.
R 10,353.52
Applied Functional Analysis
Applied Functional Analysis, Third Edition provides a solid mathematical foundation for the subject. It motivates students to study functional analysis by providing many contemporary applications and examples drawn from mechanics and science. This well-received textbook starts with a thorough introduction to modern mathematics before continuing with detailed coverage of linear algebra, Lebesque measure and integration theory, plus topology with metric spaces. The final two chapters provides readers with an in-depth look at the theory of Banach and Hilbert spaces before concluding with a brief introduction to Spectral Theory.The Third Edition is more accessible and promotes interest and motivation among students to prepare them for studying the mathematical aspects of numerical analysis and the mathematical theory of finite elements.
R 9,440.86
Foreign Policy Analysis
This succinct yet comprehensive introduction to Foreign Policy Analysis (FPA) is geared toward advanced undergraduate and beginning graduate students. Cogently written, clearly organized, and filled with illuminating examples, the third edition has been thoroughly revised and updated. Beginning with an overview of this broad field of study, Hudson and Day consider theory and research at multiple levels of analysis, including personality and psychology of foreign policy decision makers, small group dynamics, the organizational process, bureaucratic politics, domestic politics, cultural and societal influences, national attributes, and system-level effects on foreign policy. The authors also examine the promise and frustration of theoretical integration in FPA and overview promising new work by non-North American scholars.
R 1,904.00
R 4,549.00
Statistical Data Analysis
This book is a guide to the practical application of statistics in data analysis as typically encountered in the physical sciences. It is primarily addressed at students and professionals who need to draw quantitative conclusions from experimental data. Although most of the examples are taken from particle physics, the material is presented in a sufficiently general way as to be useful to people from most branches of the physical sciences. The first part of the bookdescribes the basic tools of data analysis: concepts of probability and random variables, Monte Carlo techniques, statistical tests, and methods of parameter estimation. The last three chapters are somewhat more specialized than those preceding, covering interval estimation, characteristic functions,and the problem of correcting distributions for the effects of measurement errors (unfolding).
R 2,785.00
Probabilistic and Stochastic Methods in Analysis, with Applications
Probability has been an important part of mathematics for more than three centuries. Moreover, its importance has grown in recent decades, since the computing power now widely available has allowed probabilistic and stochastic techniques to attack problems such as speech and image processing, geophysical exploration, radar, sonar, etc. -- all of which are covered here. The book contains three exceptionally clear expositions on wavelets, frames and their applications. A further extremely active current research area, well covered here, is the relation between probability and partial differential equations, including probabilistic representations of solutions to elliptic and parabolic PDEs. New approaches, such as the PDE method for large deviation problems, and stochastic optimal control and filtering theory, are beginning to yield their secrets. Another topic dealt with is the application of probabilistic techniques to mathematical analysis. Finally, there are clear explanations of normal numbers and dynamic systems, and the influence of probability on our daily lives.
R 9,031.67
Technical Analysis
Master technical analysis, step-by-step! Already the field's most comprehensive, reliable, and objective introduction, this guidebook has been thoroughly updated to reflect the field's latest advances. Selected by the Market Technicians Association as the official companion to its prestigious Chartered Market Technician (CMT) program, Technical Analysis, Third Edition systematically explains the theory of technical analysis, presenting academic evidence both for and against it. Using hundreds of fully updated illustrations and examples, the authors explain the analysis of both markets and individual issues, and present complete investment systems and portfolio management plans. They present authoritative, up-to-date coverage of tested sentiment, momentum indicators, seasonal effects, flow of funds, testing systems, risk mitigation strategies, and many other topics. Offering 30% new coverage, Technical Analysis, Third Edition thoroughly addresses recent advances in pattern recognition, market analysis, systems management, and confidence testing; Kagi, Renko, Kase, Ichimoku, Clouds, and DeMark indicators; innovations in exit stops, portfolio selection, and testing; implications of behavioral bias, and the recent performance of old formulas and methods. For traders, researchers, and serious investors alike, this is the definitive guide to profiting from technical analysis.
R 2,931.00
Trends and Challenges in Categorical Data Analysis
This book provides a selection of modern and sophisticated methodologies for the analysis of large and complex univariate and multivariate categorical data. It gives an overview of a substantive and broad collection of topics in the analysis of categorical data, including association, marginal and graphical models, time series and fixed effects models, as well as modern methods of estimation such as regularization, Bayesian estimation and bias reduction methods, along with new simple measures for model interpretability. Methodological innovations and developments are illustrated and explained through real-world applications, together with useful R packages, allowing readers to replicate most of the analyses using the provided code. The applications span a variety of disciplines, including education, psychology, health, economics, and social sciences.
R 6,161.24
Surface Analysis
Understand the principles and applications of surface analysis Analyzing the composition of a given surface is a crucial aspect of materials science, with significant bearing on the way a material will interact with a particular environment. Surface analysis draws upon a substantial body of research to analyze the outer few nanometres of a material, and has considerable significance in laboratories studying corrosion, adhesion, polymer surface treatment and more. Surface Analysis offers a comprehensive and accessible overview of the characterization and analysis of outer substrate layers. It brings together experts in each analysis area to lay out foundational theory, offer practical applications, and provide examples. Now fully up to date and reflecting the latest research, it is a must-own for any scientist incorporating surface analysis into laboratory work. Readers of the third edition of Surface Analysis readers will also find: Two new chapters on advanced mass spectrometry methodsDetailed coverage of ever-more-important methods of data analysisTools for concretely improving laboratory and research outcomes Surface Analysis is ideal for practitioners and advanced graduate students in materials science, solid state physics, optics, and chemistry.
R 5,221.00
Introductory Stochastic Analysis for Finance and Insurance
Incorporates the many tools needed for modeling and pricing in finance and insurance Introductory Stochastic Analysis for Finance and Insurance introduces readers to the topics needed to master and use basic stochastic analysis techniques for mathematical finance. The author presents the theories of stochastic processes and stochastic calculus and provides the necessary tools for modeling and pricing in finance and insurance. Practical in focus, the book's emphasis is on application, intuition, and computation, rather than theory. Consequently, the text is of interest to graduate students, researchers, and practitioners interested in these areas. While the text is self-contained, an introductory course in probability theory is beneficial to prospective readers. This book evolved from the author's experience as an instructor and has been thoroughly classroom-tested. Following an introduction, the author sets forth the fundamental information and tools needed by researchers and practitioners working in the financial and insurance industries: * Overview of Probability Theory * Discrete-Time stochastic processes * Continuous-time stochastic processes * Stochastic calculus: basic topics The final two chapters, Stochastic Calculus: Advanced Topics and Applications in Insurance, are devoted to more advanced topics. Readers learn the Feynman-Kac formula, the Girsanov's theorem, and complex barrier hitting times distributions. Finally, readers discover how stochastic analysis and principles are applied in practice through two insurance examples: valuation of equity-linked annuities under a stochastic interest rate environment and calculation of reserves for universal life insurance. Throughout the text, figures and tables are used to help simplify complex theory and pro-cesses. An extensive bibliography opens up additional avenues of research to specialized topics. Ideal for upper-level undergraduate and graduate students, this text is recommended for one-semester courses in stochastic finance and calculus. It is also recommended as a study guide for professionals taking Causality Actuarial Society (CAS) and Society of Actuaries (SOA) actuarial examinations.
R 5,469.00
Biomedical Engineering Fundamentals, Third Edition
Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product.Fully updated fundamental biomedical engineering principles and technologiesThis state-of-the-art resource offers unsurpassed coverage of fundamental concepts that enable advances in the field of biomedical engineering. Biomedical Engineering Fundamentals, Third Edition, contains all the information you need to improve efficacy and efficiency in problem solving, no matter how simple or complex the problem. Thoroughly revised by experts across the biomedical engineering discipline, this hands-on guide provides the foundational knowledge required for the development of innovative devices, techniques, and treatments.Coverage includes:Modeling of biomedical systems and heat transfer applicationsPhysical and flow properties of bloodRespiratory mechanics and gas exchangeRespiratory muscles, human movement, and the musculoskeletal systemElectromyography and muscle forcesBiopolymers, biomedical composites, and bioceramicsCardiovascular, dental, and orthopedic biomaterialsTissue regeneration and regenerative medicineBioelectricity, biomedical signal analysis, and biosensorsNeural engineering and electrical stimulation of nervous systemsCauses of medical device failure and FDA requirementsCardiovascular, respiratory, and artificial kidney devicesInfrared and ultrasound imaging, MRIs, and nuclear medicineImaging, laser Doppler, and fetal and optical monitoringComputer-integrated surgery and medical roboticsIntelligent assistive technology and rehabilitatorsArtificial limbs, hip and knee replacement, and sensory augmentationHealthcare systems engineering and medical informaticsHospital information systems and computer-based patient recordsSterile medical device package development
R 6,049.81
Athens Conference on Applied Probability and Time Series Analysis
The Athens Conference on Applied Probability and Time Series in 1995 brought together researchers from across the world. The published papers appear in two volumes. Volume I includes papers on applied probability in Honor of J.M. Gani. The topics include probability and probabilistic methods in recursive algorithms and stochastic models, Markov and other stochastic models such as Markov chains, branching processes and semi-Markov systems, biomathematical and genetic models, epidemilogical models including S-I-R (Susceptible-Infective-Removal), household and AIDS epidemics, financial models for option pricing and optimization problems, random walks, queues and their waiting times, and spatial models for earthquakes and inference on spatial models.
R 3,064.52
Probabilistic Analysis of Belief Functions
Inspired by the eternal beauty and truth of the laws governing the run of stars on heavens over his head, and spurred by the idea to catch, perhaps for the smallest fraction of the shortest instant, the Eternity itself, man created such masterpieces of human intellect like the Platon's world of ideas manifesting eternal truths, like the Euclidean geometry, or like the Newtonian celestial me chanics. However, turning his look to the sub-lunar world of our everyday efforts, troubles, sorrows and, from time to time but very, very seldom, also our successes, he saw nothing else than a world full of uncertainty and tem porariness. One remedy or rather consolation was that of the deep and sage resignation offered by Socrates: I know, that I know nothing. But, happy or unhappy enough, the temptation to see and to touch at least a very small por tion of eternal truth also under these circumstances and behind phenomena charged by uncertainty was too strong. Probability theory in its most sim ple elementary setting entered the scene. It happened in the same, 17th and 18th centuries, when celestial mechanics with its classical Platonist paradigma achieved its greatest triumphs. The origins of probability theory were inspired by games of chance like roulettes, lotteries, dices, urn schemata, etc. and probability values were simply defined by the ratio of successful or winning results relative to the total number of possible outcomes.
R 3,028.07
Sensitivity Analysis
The Wiley Paperback Series makes valuable content more accessible to a new generation of statisticians, mathematicians and scientists. Sensitivity analysis is used to ascertain how a given model output depends upon the input parameters. This is an important method for checking the quality of a given model, as well as a powerful tool for checking the robustness and reliability of its analysis. The topic is acknowledged as essential for good modelling practice and is an implicit part of any modelling field. Offers an accessible introduction to sensitivity analysis.Covers all the latest research.Illustrates concepts with numerous examples, applications and case studies.Includes contributions from the leading researchers active in developing strategies for sensitivity analysis. The principles of sensitivity analysis are carefully described and suitable methods for approaching many types of problems are given. The book introduces the modeller to the entire casual assessment chain, from data to predictions, whilst explaining the impact of source uncertainties and framing assumptions. A ‘hitch-hikers guide’ is included to allow the more experienced reader to readily access specific applications. Modellers from a wide range of disciplines, including biostatistics, economics, environmental impact assessment, chemistry and engineering will benefit greatly from the numerous examples and applications. "Presents many different sensitivity analysis methodologies and demonstrates their usefulness in scientific research." (Zentralblatt MATH)
R 2,995.00
Comparison of the Bayesian and Frequentist Approaches to Estimation
The main theme of this monograph is “comparative statistical inference. ” While the topics covered have been carefully selected (they are, for example, restricted to pr- lems of statistical estimation), my aim is to provide ideas and examples which will assist a statistician, or a statistical practitioner, in comparing the performance one can expect from using either Bayesian or classical (aka, frequentist) solutions in - timation problems. Before investing the hours it will take to read this monograph, one might well want to know what sets it apart from other treatises on comparative inference. The two books that are closest to the present work are the well-known tomes by Barnett (1999) and Cox (2006). These books do indeed consider the c- ceptual and methodological differences between Bayesian and frequentist methods. What is largely absent from them, however, are answers to the question: “which - proach should one use in a given problem?” It is this latter issue that this monograph is intended to investigate. There are many books on Bayesian inference, including, for example, the widely used texts by Carlin and Louis (2008) and Gelman, Carlin, Stern and Rubin (2004). These books differ from the present work in that they begin with the premise that a Bayesian treatment is called for and then provide guidance on how a Bayesian an- ysis should be executed. Similarly, there are many books written from a classical perspective.
R 3,027.31
Basic Concepts of Data and Error Analysis
This introductory textbook explains the concepts and methods of data and error analysis needed for laboratory experiment write-ups, especially physics and engineering experiments. The book contains the material needed for beginning students, e.g., first year university students, college students (enrolled on a certificate or diploma course) and even A-level students. Nevertheless, it also covers the required material for higher year university laboratories, including the final year. Only essential concepts and methods needed for the day-to-day performance of experiments and their subsequent analysis and presentation are included and, at the same time, presented as simply as possible. Non-essential detail is avoided. Chapter five is a stand-alone introduction to probability and statistics aimed at providing a theoretical background to the data and error analysis chapters one to four. Computer methods are introduced in Chapter six. The author hopes this book will serve as a constant reference.
R 1,649.00
Computational Analysis
Featuring the clearly presented and expertly-refereed contributions of leading researchers in the field of approximation theory, this volume is a collection of the best contributions at the Third International Conference on Applied Mathematics and Approximation Theory, an international conference held at TOBB University of Economics and Technology in Ankara, Turkey, on May 28-31, 2015.The goal of the conference, and this volume, is to bring together key work from researchers in all areas of approximation theory, covering topics such as ODEs, PDEs, difference equations, applied analysis, computational analysis, signal theory, positive operators, statistical approximation, fuzzy approximation, fractional analysis, semigroups, inequalities, special functions and summability. These topics are presented both within their traditional context of approximation theory, while also focusing on their connections to applied mathematics. As a result, this collection will be an invaluable resource for researchers in applied mathematics, engineering and statistics.
R 5,549.21
R 1,963.41
Theory of Sampling and Sampling Practice, Third Edition
A step-by-step guide for anyone challenged by the many subtleties of sampling particulate materials. The only comprehensive document merging the famous works of P. Gy, I. Visman, and C.O. Ingamells into a single theory in a logical way - the most advanced book on sampling that can be used by all sampling practitioners around the world.
R 14,048.83
Nonlinear Finite Element Analysis of Solids and Structures
Built upon the two original books by Mike Crisfield and their own lecture notes, renowned scientist René de Borst and his team offer a thoroughly updated yet condensed edition that retains and builds upon the excellent reputation and appeal amongst students and engineers alike for which Crisfield's first edition is acclaimed. Together with numerous additions and updates, the new authors have retained the core content of the original publication, while bringing an improved focus on new developments and ideas. This edition offers the latest insights in non-linear finite element technology, including non-linear solution strategies, computational plasticity, damage mechanics, time-dependent effects, hyperelasticity and large-strain elasto-plasticity. The authors' integrated and consistent style and unrivalled engineering approach assures this book's unique position within the computational mechanics literature. Key features: Combines the two previous volumes into one heavily revised text with obsolete material removed, an improved layout and updated references and notationsExtensive new material on more recent developments in computational mechanicsEasily readable, engineering oriented, with no more details in the main text than necessary to understand the concepts.Pseudo-code throughout makes the link between theory and algorithms, and the actual implementation.Accompanied by a website (www.wiley.com/go/deborst) with a Python code, based on the pseudo-code within the book and suitable for solving small-size problems. Non-linear Finite Element Analysis of Solids and Structures, 2nd Edition is an essential reference for practising engineers and researchers that can also be used as a text for undergraduate and graduate students within computational mechanics.
R 4,134.00
Electrodynamics Tutorials with Python Simulations
This book provides an accessible introduction to intermediate-level electrodynamics with computational approaches to complement a traditional mathematical treatment of the subject. It covers key topics in electrodynamics, such as electromagnetic fields, forces, potentials, and waves as well as Special Theory of Relativity.Through intuition-building examples and visualizations in the Python programming language, it helps readers to develop technical computing skills in numerical and symbolic calculations, modeling and simulations, and visualizations. Python is a highly readable and practical programming language, making this book appropriate for students without extensive programming experience.This book can serve as an electrodynamics textbook for undergraduate physics and engineering students in their second or third years, who are studying intermediate- or advanced-level electrodynamics and who want to learn techniques for scientific computing at the same time. This book will also appeal to computer science students who want to see how their computer programming skills may be applied to science, particularly to physics, without needing too much background physics knowledge.Key features● Major concepts in classical electrodynamics are introduced cohesively through computational and mathematical treatments.● Computational examples in Python programming language guide students on how to simulate and visualize electrodynamic principles and phenomena for themselves.Taejoon Kouh is a Professor of Nano and Electronic Physics at Kookmin University, Republic of Korea. He earned his B.A. in physics from Boston University and Sc.M. and Ph.D. degrees in physics from Brown University. After his study in Providence, RI, he returned to Boston, MA, and worked as a postdoctoral research associate in the Department of Aerospace and Mechanical Engineering at Boston University. He is a full faculty member in the Department of Nano and Electronic Physics at Kookmin University in Seoul, Korea, teaching and supervising undergraduate and graduate students. His current research involves the dynamics of nanoelectromechanical systems and the development of fast and reliable transduction methods and innovative applications based on tiny motion.Minjoon Kouh is a program scientist for a philanthropic initiative. He was a Professor of Physics and Neuroscience at Drew University, USA, where he taught more than 30 distinct types of courses. He holds Ph.D. and B.S. degrees in physics from MIT and an M.A. from UC Berkeley. He completed a postdoctoral research fellowship at the Salk Institute for Biological Studies in La Jolla, CA. His research includes computational modeling of the primate visual cortex, information-theoretic analysis of neural responses, machine learning, and pedagogical innovations in undergraduate science education.
R 7,580.14
Meta-Analysis of Controlled Clinical Trials
Over the last twenty years there has been a dramatic upsurge in the application of meta-analysis to medical research. This has mainly been due to greater emphasis on evidence-based medicine and the need for reliable summaries of the vast and expanding volume of clinical research. At the same time there have been great strides in the development and refinement of the associated statistical methodology. This book describes the planning, conduct and reporting of a meta-analysis as applied to a series of randomized controlled clinical trials. The various approaches are presented within a general unified framework.Meta-analysis techniques are described in detail, from their theoretical development through to practical implementation.Each topic discussed is supported by detailed worked examples.A comparison of fixed and random effects approaches is included, as well as a discussion of Bayesian methods and cumulative meta-analysis.Fully documented programs using standard statistical procedures in SAS are available on the Web. Ideally suited for practising statisticians and statistically-minded medical professionals, the book will also be of use to graduate students of medical statistics. The book is a self-contained and comprehensive account of the subject and an essential purchase for anyone involved in clinical trials.
R 5,925.00
AI for Games, Third Edition
AI is an integral part of every video game and this book helps game developers keep up with the constantly evolving technological advances to create robust AI. The authors draw on their considerable experience and uses case studies from real games to provide a complete reference. Also included are exercises so readers can test their comprehension and understanding of the concepts and practices presented.This revised and updated Third Edition includes new techniques, algorithms, data structures and representations needed to create powerful AI in games. It helps experienced game developers learn new techniques and provides students with a solid understanding of game AI that will help them jumpstart their careers.
R 1,903.00
Electromagnetic Simulation Using the FDTD Method with Python
Provides an introduction to the Finite Difference Time Domain method and shows how Python code can be used to implement various simulations This book allows engineering students and practicing engineers to learn the finite-difference time-domain (FDTD) method and properly apply it toward their electromagnetic simulation projects. Each chapter contains a concise explanation of an essential concept and instruction on its implementation into computer code. Included projects increase in complexity, ranging from simulations in free space to propagation in dispersive media. This third edition utilizes the Python programming language, which is becoming the preferred computer language for the engineering and scientific community. Electromagnetic Simulation Using the FDTD Method with Python, Third Edition is written with the goal of enabling readers to learn the FDTD method in a manageable amount of time. Some basic applications of signal processing theory are explained to enhance the effectiveness of FDTD simulation. Topics covered in include one-dimensional simulation with the FDTD method, two-dimensional simulation, and three-dimensional simulation. The book also covers advanced Python features and deep regional hyperthermia treatment planning. Electromagnetic Simulation Using the FDTD Method with Python: Guides the reader from basic programs to complex, three-dimensional programs in a tutorial fashionIncludes a rewritten fifth chapter that illustrates the most interesting applications in FDTD and the advanced graphics techniques of PythonCovers peripheral topics pertinent to time-domain simulation, such as Z-transforms and the discrete Fourier transformProvides Python simulation programs on an accompanying website An ideal book for senior undergraduate engineering students studying FDTD, Electromagnetic Simulation Using the FDTD Method with Python will also benefit scientists and engineers interested in the subject.
R 3,883.00
Design and Optimization of Thermal Systems, Third Edition
Design and Optimization of Thermal Systems, Third Edition: with MATLAB® Applications provides systematic and efficient approaches to the design of thermal systems, which are of interest in a wide range of applications. It presents basic concepts and procedures for conceptual design, problem formulation, modeling, simulation, design evaluation, achieving feasible design, and optimization. Emphasizing modeling and simulation, with experimentation for physical insight and model validation, the third edition covers the areas of material selection, manufacturability, economic aspects, sensitivity, genetic and gradient search methods, knowledge-based design methodology, uncertainty, and other aspects that arise in practical situations. This edition features many new and revised examples and problems from diverse application areas and more extensive coverage of analysis and simulation with MATLAB®.
R 7,697.57
Applied Behavior Analysis
This text provides an accurate, comprehensive, and contemporary description of applied behavior analysis in order to help readers acquire fundamental knowledge and skills Applied Behavior Analysis provides a comprehensive, in-depth discussion of the field, offering a complete description of the principles and procedures for changing and analyzing socially important behavior. The 3rd Edition features coverage of advances in all three interrelated domains of the sciences of behavior–theoretical, basic research, and applied research–and two new chapters, Equivalence-based Instruction (Ch. 19) and Engineering Emergent Learning with Nonequivalence Relations (Ch. 20). It also includes updated and new content on topics such as negative reinforcement (Ch. 12), motivation (Ch. 16), verbal behavior (Ch. 18), functional behavioral assessment (Ch. 27), and ethics (Ch. 31). The content of the text is now connected to the BCBA® and BCABA® Behavior Analyst Task List, 5th Edition.
R 6,702.00
Performance Analysis and Modeling of Digital Transmission Systems
This book is an expanded third edition of the book Performance Analysis of Digital Transmission Systems, originally published in 1990. Second edition of the book titled Digital Transmission Systems: Performance Analysis and Modeling was published in 1998. The book is intended for those who design communication systems and networks. A computer network designer is interested in selecting communication channels, error protection schemes, and link control protocols. To do this efficiently, one needs a mathematical model that accurately predicts system behavior. Two basic problems arise in mathematical modeling: the problem of identifying a system and the problem of applying a model to the system analysis. System identification consists of selecting a class of mathematical objects to describe fundamental properties of the system behavior. We use a specific class of hidden Markov models (HMMs) to model communication systems. This model was introduced by C. E. Shannon more than 50 years ago as a Noisy Discrete Channel with a finite number of states. The model is described by a finite number of matrices whose elements are estimated on the basis of experimental data. We develop several methods of model identification and show their relationship to other methods of data analysis, such as spectral methods, autoregressive moving average CARMA) approximations, and rational transfer function approximations.
R 1,620.53
Analysis of Capture-Recapture Data
An important first step in studying the demography of wild animals is to identify the animals uniquely through applying markings, such as rings, tags, and bands. Once the animals are encountered again, researchers can study different forms of capture-recapture data to estimate features, such as the mortality and size of the populations. Capture-recapture methods are also used in other areas, including epidemiology and sociology.With an emphasis on ecology, Analysis of Capture-Recapture Data covers many modern developments of capture-recapture and related models and methods and places them in the historical context of research from the past 100 years. The book presents both classical and Bayesian methods.A range of real data sets motivates and illustrates the material and many examples illustrate biometry and applied statistics at work. In particular, the authors demonstrate several of the modeling approaches using one substantial data set from a population of great cormorants. The book also discusses which computer programs to use for implementing the models and contains 130 exercises that extend the main material. The data sets, computer programs, and other ancillaries are available at www.capturerecapture.co.uk.The book is accessible to advanced undergraduate and higher-level students, quantitative ecologists, and statisticians. It helps readers understand model formulation and applications, including the technicalities of model diagnostics and checking.
R 5,647.28
Hands-On Web Scraping with Python
Work through practical examples to unlock the full potential of web scraping with Python and gain valuable insights from high-quality dataKey FeaturesBuild an initial portfolio of web scraping projects with detailed explanationsGrasp Python programming fundamentals related to web scraping and data extractionAcquire skills to code web scrapers, store data in desired formats, and employ the data professionallyPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionWeb scraping is a powerful tool for extracting data from the web, but it can be daunting for those without a technical background. Designed for novices, this book will help you grasp the fundamentals of web scraping and Python programming, even if you have no prior experience.Adopting a practical, hands-on approach, this updated edition of Hands-On Web Scraping with Python uses real-world examples and exercises to explain key concepts. Starting with an introduction to web scraping fundamentals and Python programming, you’ll cover a range of scraping techniques, including requests, lxml, pyquery, Scrapy, and Beautiful Soup. You’ll also get to grips with advanced topics such as secure web handling, web APIs, Selenium for web scraping, PDF extraction, regex, data analysis, EDA reports, visualization, and machine learning.This book emphasizes the importance of learning by doing. Each chapter integrates examples that demonstrate practical techniques and related skills. By the end of this book, you’ll be equipped with the skills to extract data from websites, a solid understanding of web scraping and Python programming, and the confidence to use these skills in your projects for analysis, visualization, and information discovery.What you will learnMaster web scraping techniques to extract data from real-world websitesImplement popular web scraping libraries such as requests, lxml, Scrapy, and pyqueryDevelop advanced skills in web scraping, APIs, PDF extraction, regex, and machine learningAnalyze and visualize data with Pandas and PlotlyDevelop a practical portfolio to demonstrate your web scraping skillsUnderstand best practices and ethical concerns in web scraping and data extractionWho this book is forThis book is for beginners who want to learn web scraping and data extraction using Python. No prior programming knowledge is required, but a basic understanding of web-related concepts such as websites, browsers, and HTML is assumed. If you enjoy learning by doing and want to build a portfolio of web scraping projects and delve into data-related studies and application, then this book is tailored for your needs.
R 1,531.00
Multiphase Flows with Droplets and Particles, Third Edition
Multiphase Flows with Droplets and Particles provides an organized, pedagogical study of multiphase flows with particles and droplets. This revised edition presents new information on particle interactions, particle collisions, thermophoresis and Brownian movement, computational techniques and codes, and the treatment of irregularly shaped particles. An entire chapter is devoted to the flow of nanoparticles and applications of nanofluids. Features Discusses the modelling and analysis of nanoparticles. Covers all fundamental aspects of particle and droplet flows. Includes heat and mass transfer processes. Features new and updated sections throughout the text. Includes chapter exercises and a Solutions Manual for adopting instructors.Designed to complement a graduate course in multiphase flows, the book can also serve as a supplement in short courses for engineers or as a stand-alone reference for engineers and scientists who work in this area.
R 3,738.72
R 611.00
