Search results for "bayesian analysis with python - third edition"
Items we need to order for you
Evaluation Essentials, Third Edition
Beloved for its conversational style and reliable advice, this text is now in a revised and updated third edition, reflecting key developments in evaluation. It includes expanded coverage of equity and social justice issues, values and cost analysis, visualizing qualitative data with software, and more. Twenty-six concise chapters, or "sessions," give students, applied researchers, and program administrators a solid foundation for conducting or using evaluations. Covering both quantitative and qualitative methods, the book emphasizes fostering evaluation use. It shows how to build collaborative relationships with users; formulate answerable evaluation questions; deal with contingencies that might alter the traditional sequence of an evaluation; and collect, analyze, and report data. Student-friendly features throughout the sessions include titles written as questions, bulleted recaps, "Thinking Ahead" and "Next Steps" pointers, cautionary notes, and annotated suggestions for further reading. An in-depth case study provides the basis for end-of-session practice exercises. New to This Edition *New sessions on context-sensitive evaluation, including the organizational, sociopolitical, and community contexts of a program. *New or expanded discussions of timely topics: identifying evaluation decision makers, analyzing program costs, coding and visualizing qualitative data with software, and more. *Updated suggestions for further reading and discussion in every chapter.
R 2,125.00
Regression Models for Time Series Analysis
A thorough review of the most current regression methods in time series analysis Regression methods have been an integral part of time series analysis for over a century. Recently, new developments have made major strides in such areas as non-continuous data where a linear model is not appropriate. This book introduces the reader to newer developments and more diverse regression models and methods for time series analysis. Accessible to anyone who is familiar with the basic modern concepts of statistical inference, Regression Models for Time Series Analysis provides a much-needed examination of recent statistical developments. Primary among them is the important class of models known as generalized linear models (GLM) which provides, under some conditions, a unified regression theory suitable for continuous, categorical, and count data. The authors extend GLM methodology systematically to time series where the primary and covariate data are both random and stochastically dependent. They introduce readers to various regression models developed during the last thirty years or so and summarize classical and more recent results concerning state space models. To conclude, they present a Bayesian approach to prediction and interpolation in spatial data adapted to time series that may be short and/or observed irregularly. Real data applications and further results are presented throughout by means of chapter problems and complements. Notably, the book covers: * Important recent developments in Kalman filtering, dynamic GLMs, and state-space modeling * Associated computational issues such as Markov chain, Monte Carlo, and the EM-algorithm * Prediction and interpolation * Stationary processes
R 6,902.00
Methods of Multivariate Analysis
Praise for the Second Edition "This book is a systematic, well-written, well-organized text on multivariate analysis packed with intuition and insight . . . There is much practical wisdom in this book that is hard to find elsewhere." IIE Transactions Filled with new and timely content, Methods of Multivariate Analysis, Third Edition provides examples and exercises based on more than sixty real data sets from a wide variety of scientific fields. It takes a "methods" approach to the subject, placing an emphasis on how students and practitioners can employ multivariate analysis in real-life situations. This Third Edition continues to explore the key descriptive and inferential procedures that result from multivariate analysis. Following a brief overview of the topic, the book goes on to review the fundamentals of matrix algebra, sampling from multivariate populations, and the extension of common univariate statistical procedures (including t-tests, analysis of variance, and multiple regression) to analogous multivariate techniques that involve several dependent variables. The latter half of the book describes statistical tools that are uniquely multivariate in nature, including procedures for discriminating among groups, characterizing low-dimensional latent structure in high-dimensional data, identifying clusters in data, and graphically illustrating relationships in low-dimensional space. In addition, the authors explore a wealth of newly added topics, including: Confirmatory Factor AnalysisClassification TreesDynamic GraphicsTransformations to NormalityPrediction for Multivariate Multiple RegressionKronecker Products and Vec Notation New exercises have been added throughout the book, allowing readers to test their comprehension of the presented material. Detailed appendices provide partial solutions as well as supplemental tables, and an accompanying FTP site features the book's data sets and related SAS® code. Requiring only a basic background in statistics, Methods of Multivariate Analysis, Third Edition is an excellent book for courses on multivariate analysis and applied statistics at the upper-undergraduate and graduate levels. The book also serves as a valuable reference for both statisticians and researchers across a wide variety of disciplines.
R 4,851.00
Data Visualization and Analysis in Second Language Research
This introduction to visualization techniques and statistical models for second language research focuses on three types of data (continuous, binary, and scalar), helping readers to understand regression models fully and to apply them in their work. Garcia offers advanced coverage of Bayesian analysis, simulated data, exercises, implementable script code, and practical guidance on the latest R software packages. The book, also demonstrating the benefits to the L2 field of this type of statistical work, is a resource for graduate students and researchers in second language acquisition, applied linguistics, and corpus linguistics who are interested in quantitative data analysis.
R 9,317.62
Sensitivity Analysis in Practice
Sensitivity analysis is the study of how variation in the output of a statistical model can be apportioned, qualitatively or quantitatively, to different sources of variation. It should be considered a pre-requisite for statistical model building in any scientific discipline where modelling takes place. Choosing the most appropriate method of sensitivity analysis for a particular model can be complex, and depends on a number of factors. Sensitivity Analysis in Practice guides applied scientists through their modelling problem enabling them to choose and apply the most appropriate sensitivity analysis method. It offers a review of the state-of-the-art in sensitivity analysis, and is suitable for a wide range of practitioners. It includes discussion of implementation of the methods using SIMLAB -- freely available sensitivity analysis software. Provides an accessible overview of the most widely used sensitivity analysis methods. Covers a range of methodologies, including variance-based methods, Bayesian uncertainty estimation, selected screening methods and Monte Carlo filtering. Opens with a detailed worked example to explain the motivation behind methods described.Includes a range
R 3,906.00
Scenario Analysis in Risk Management
This book focuses on identifying and explaining the key determinants of scenario analysis in the context of operational risk, stress testing and systemic risk, as well as management and planning. Each chapter presents alternative solutions to perform reliable scenario analysis. The author also provides technical notes and describes applications and key characteristics for each of the solutions. In addition, the book includes a section to help practitioners interpret the results and adjust them to real-life management activities. Methodologies, including those derived from consensus strategies, extreme value theory, Bayesian networks, Neural networks, Fault Trees, frequentist statistics and data mining are introduced in such a way as to make them understandable to readers without a quantitative background. Particular emphasis is given to the added value of the implementation of these methodologies.
R 4,629.27
Statistical Analysis of Spatial and Spatio-Temporal Point Patterns
Written by a prominent statistician and author, the first edition of this bestseller broke new ground in the then emerging subject of spatial statistics with its coverage of spatial point patterns. Retaining all the material from the second edition and adding substantial new material, Statistical Analysis of Spatial and Spatio-Temporal Point Patterns, Third Edition presents models and statistical methods for analyzing spatially referenced point process data.Reflected in the title, this third edition now covers spatio-temporal point patterns. It explores the methodological developments from the last decade along with diverse applications that use spatio-temporally indexed data. Practical examples illustrate how the methods are applied to analyze spatial data in the life sciences.This edition also incorporates the use of R through several packages dedicated to the analysis of spatial point process data. Sample R code and data sets are available on the author’s website.
R 5,644.78
Introduction to Survey Research, Polling, and Data Analysis
"The writing style is mature and experienced. There is noticeable care in the presentation of materials and appropriate sensitivity to the intended audience. . . . The book categorizes large bodies of wisdom in the conduct of survey research over the past half century and provides ′′rules-of-thumb′′ guidance to the production, analysis, and consumption of survey data." --Steve Seitz, University of Illinois "The authors have provided a well-written update of their textbook, which will be useful as a textbook or supplement in upper-division undergraduate or introductory graduate courses on survey research, statistical data analysis, or research design. The book provides readers with the knowledge that enables them to evaluate the results of survey research. It sensitizes the readers to potential problems that arise from flaws in research design, sampling, questionnaire construction, data collection, coding, data analysis techniques, and improper presentation or interpretation of findings." --Mike Margolis, University of Cincinnati Why should we believe the results of surveys based on relatively few interviews? What are the appropriate cautions in interpreting survey reports? Aimed at answering these questions, An Introduction to Survey Research, Polling, and Data Analysis, Third Edition describes how surveys are conducted in such a way that they can be believed, explains how to read statistical reports and analyze data, and provides guidelines that are useful in evaluating polls. Using frequent examples from contemporary, large national surveys and polls as well as from the media, the authors stress understanding tables thoroughly before moving to interval statistics. In addition, they cover survey design, sampling and question-writing steps, interviewing and coding strategies, and survey analysis from frequency distributions and cross-tabulations to control tables and correlation/regression. They also cover the ethics of survey research and how to read and write survey research reports. Readers of this book will learn to distinguish good from bad polls and to better design and conduct their own surveys and polls.
R 4,937.57
Basin Analysis
Basin Analysis is an advanced undergraduate and postgraduate text aimed at understanding sedimentary basins as geodynamic entities. The rationale of the book is that knowledge of the basic principles of the thermo-mechanical behaviour of the lithosphere, the dynamics of the mantle, and the functioning of sediment routing systems provides a sound background for studying sedimentary basins, and is a pre-requisite for the exploitation of resources contained in their sedimentary rocks. The third edition incorporates new developments in the burgeoning field of basin analysis while retaining the successful structure and overall philosophy of the first two editions. The text is divided into 4 parts that establish the geodynamical environment for sedimentary basins and the physical state of the lithosphere, followed by a coverage of the mechanics of basin formation, an integrated analysis of the controls on the basin-fill and its burial and thermal history, and concludes with an application of basin analysis principles in petroleum play assessment, including a discussion of unconventional hydrocarbon plays. The text is richly supplemented by Appendices providing mathematical derivations of a wide range of processes affecting the formation of basins and their sedimentary fills. Many of these Appendices include practical exercises that give the reader hands-on experience of quantitative solutions to important basin analysis processes. Now in full colour and a larger format, this third edition is a comprehensive update and expansion of the previous editions, and represents a rigorous yet accessible guide to problem solving in this most integrative of geoscientific disciplines. Additional resources for this book can be found at: www.wiley.com/go/allen/basinanalysis.
R 3,353.00
Specification Analysis in the Linear Model
Originally published in 1987. This collection of original papers deals with various issues of specification in the context of the linear statistical model. The volume honours the early econometric work of Donald Cochrane, late Dean of Economics and Politics at Monash University in Australia. The chapters focus on problems associated with autocorrelation of the error term in the linear regression model and include appraisals of early work on this topic by Cochrane and Orcutt. The book includes an extensive survey of autocorrelation tests; some exact finite-sample tests; and some issues in preliminary test estimation. A wide range of other specification issues is discussed, including the implications of random regressors for Bayesian prediction; modelling with joint conditional probability functions; and results from duality theory. There is a major survey chapter dealing with specification tests for non-nested models, and some of the applications discussed by the contributors deal with the British National Accounts and with Australian financial and housing markets.
R 1,246.00
Introduction to Quantitative Social Science with Python
Departing from traditional methodologies of teaching data analysis, this book presents a dual-track learning experience, with both Executive and Technical Tracks, designed to accommodate readers with various learning goals or skill levels. Through integrated content, readers can explore fundamental concepts in data analysis while gaining hands-on experience with Python programming, ensuring a holistic understanding of theory and practical application in Python.Emphasizing the practical relevance of data analysis in today's world, the book equips readers with essential skills for success in the field. By advocating for the use of Python, an open-source and versatile programming language, we break down financial barriers and empower a diverse range of learners to access the tools they need to excel.Whether you're a novice seeking to grasp the foundational concepts of data analysis or a seasoned professional looking to enhance your programming skills, this book offers a comprehensive and accessible guide to mastering the art and science of data analysis in social science research.Key Features:Dual-track learning: Offers both Executive and Technical Tracks, catering to readers with varying levels of conceptual and technical proficiency in data analysis.Includes comprehensive quantitative methodologies for quantitative social science studies.Seamless integration: Interconnects key concepts between tracks, ensuring a smooth transition from theory to practical implementation for a comprehensive learning experience.Emphasis on Python: Focuses on Python programming language, leveraging its accessibility, versatility, and extensive online support to equip readers with valuable data analysis skills applicable across diverse domains.
R 9,328.33
Data Science with Python
Leverage the power of the Python data science libraries and advanced machine learning techniques to analyse large unstructured datasets and predict the occurrence of a particular future event.Key FeaturesExplore the depths of data science, from data collection through to visualizationLearn pandas, scikit-learn, and Matplotlib in detailStudy various data science algorithms using real-world datasetsBook DescriptionData Science with Python begins by introducing you to data science and teaches you to install the packages you need to create a data science coding environment. You will learn three major techniques in machine learning: unsupervised learning, supervised learning, and reinforcement learning. You will also explore basic classification and regression techniques, such as support vector machines, decision trees, and logistic regression.As you make your way through chapters, you will study the basic functions, data structures, and syntax of the Python language that are used to handle large datasets with ease. You will learn about NumPy and pandas libraries for matrix calculations and data manipulation, study how to use Matplotlib to create highly customizable visualizations, and apply the boosting algorithm XGBoost to make predictions. In the concluding chapters, you will explore convolutional neural networks (CNNs), deep learning algorithms used to predict what is in an image. You will also understand how to feed human sentences to a neural network, make the model process contextual information, and create human language processing systems to predict the outcome.By the end of this book, you will be able to understand and implement any new data science algorithm and have the confidence to experiment with tools or libraries other than those covered in the book.What you will learnPre-process data to make it ready to use for machine learningCreate data visualizations with MatplotlibUse scikit-learn to perform dimension reduction using principal component analysis (PCA)Solve classification and regression problemsGet predictions using the XGBoost libraryProcess images and create machine learning models to decode them Process human language for prediction and classificationUse TensorBoard to monitor training metrics in real timeFind the best hyperparameters for your model with AutoMLWho this book is forData Science with Python is designed for data analysts, data scientists, database engineers, and business analysts who want to move towards using Python and machine learning techniques to analyze data and predict outcomes. Basic knowledge of Python and data analytics will prove beneficial to understand the various concepts explained through this book.
R 1,335.00
Hands-On Simulation Modeling with Python
Enhance your simulation modeling skills by creating and analyzing digital prototypes of a physical model using Python programming with this comprehensive guideKey FeaturesLearn to create a digital prototype of a real model using hands-on examplesEvaluate the performance and output of your prototype using simulation modeling techniquesUnderstand various statistical and physical simulations to improve systems using PythonBook DescriptionSimulation modeling helps you to create digital prototypes of physical models to analyze how they work and predict their performance in the real world. With this comprehensive guide, you'll understand various computational statistical simulations using Python.Starting with the fundamentals of simulation modeling, you'll understand concepts such as randomness and explore data generating processes, resampling methods, and bootstrapping techniques. You'll then cover key algorithms such as Monte Carlo simulations and Markov decision processes, which are used to develop numerical simulation models, and discover how they can be used to solve real-world problems. As you advance, you'll develop simulation models to help you get accurate results and enhance decision-making processes. Using optimization techniques, you'll learn to modify the performance of a model to improve results and make optimal use of resources. The book will guide you in creating a digital prototype using practical use cases for financial engineering, prototyping project management to improve planning, and simulating physical phenomena using neural networks.By the end of this book, you'll have learned how to construct and deploy simulation models of your own to overcome real-world challenges.What you will learnGain an overview of the different types of simulation modelsGet to grips with the concepts of randomness and data generation processUnderstand how to work with discrete and continuous distributionsWork with Monte Carlo simulations to calculate a definite integralFind out how to simulate random walks using Markov chainsObtain robust estimates of confidence intervals and standard errors of population parametersDiscover how to use optimization methods in real-life applicationsRun efficient simulations to analyze real-world systemsWho this book is forHands-On Simulation Modeling with Python is for simulation developers and engineers, model designers, and anyone already familiar with the basic computational methods that are used to study the behavior of systems. This book will help you explore advanced simulation techniques such as Monte Carlo methods, statistical simulations, and much more using Python. Working knowledge of Python programming language is required.
R 2,214.00
Introduction to Computer-Intensive Methods of Data Analysis in Biology
This 2006 guide to the contemporary toolbox of methods for data analysis will serve graduate students and researchers across the biological sciences. Modern computational tools, such as Maximum Likelihood, Monte Carlo and Bayesian methods, mean that data analysis no longer depends on elaborate assumptions designed to make analytical approaches tractable. These new 'computer-intensive' methods are currently not consistently available in statistical software packages and often require more detailed instructions. The purpose of this book therefore is to introduce some of the most common of these methods by providing a relatively simple description of the techniques. Examples of their application are provided throughout, using real data taken from a wide range of biological research. A series of software instructions for the statistical software package S-PLUS are provided along with problems and solutions for each chapter.
R 2,733.04
Analysis
Hauptthema dieses zweiten Bandes ist die Differential- und Integralrechnung für Funktionen von mehreren Veränderlichen. Dabei wird auch das Lebesguesche Integral im Rn behandelt. Dem erfolgreichen Konzept von "Analysis 1" folgend, wird viel Wert auf historische Zusammenhänge, Ausblicke und die Entwicklung der Analysis gelegt. Zu den Besonderheiten, die über den kanonischen Stoff des zweiten Semesters hinausgehen, gehören das Morsesche und das Sardsche Lemma, die C?-Approximation von Funktionen (Mollifiers) und die Theorie der absolutstetigen Funktionen. Zahlreiche Beispiele, Übungsaufgaben und Anwendungen, z.B. aus der Physik und Astronomie, runden dieses Lehrbuch ab. Der Abschnitt "Lösungen und Lösungshinweise" wurde für die Neuauflage wesentlich erweitert, so daß die überwiegende Zahl der Aufgaben im Buch nun besprochen oder vollständig gelöst wird.
R 2,003.63
Numerical Bayesian Methods Applied to Signal Processing
This book is concerned with the processing of signals that have been sam pled and digitized. The fundamental theory behind Digital Signal Process ing has been in existence for decades and has extensive applications to the fields of speech and data communications, biomedical engineering, acous tics, sonar, radar, seismology, oil exploration, instrumentation and audio signal processing to name but a few [87]. The term "Digital Signal Processing", in its broadest sense, could apply to any operation carried out on a finite set of measurements for whatever purpose. A book on signal processing would usually contain detailed de scriptions of the standard mathematical machinery often used to describe signals. It would also motivate an approach to real world problems based on concepts and results developed in linear systems theory, that make use of some rather interesting properties of the time and frequency domain representations of signals. While this book assumes some familiarity with traditional methods the emphasis is altogether quite different. The aim is to describe general methods for carrying out optimal signal processing.
R 5,994.66
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 2,759.38
Course in Time Series Analysis
New statistical methods and future directions of research in time series A Course in Time Series Analysis demonstrates how to build time series models for univariate and multivariate time series data. It brings together material previously available only in the professional literature and presents a unified view of the most advanced procedures available for time series model building. The authors begin with basic concepts in univariate time series, providing an up-to-date presentation of ARIMA models, including the Kalman filter, outlier analysis, automatic methods for building ARIMA models, and signal extraction. They then move on to advanced topics, focusing on heteroscedastic models, nonlinear time series models, Bayesian time series analysis, nonparametric time series analysis, and neural networks. Multivariate time series coverage includes presentations on vector ARMA models, cointegration, and multivariate linear systems. Special features include: Contributions from eleven of the worldâ??s leading figures in time seriesShared balance between theory and applicationExercise series setsMany real data examplesConsistent style and clear, common notation in all contributions60 helpful graphs and tables Requiring no previous knowledge of the subject, A Course in Time Series Analysis is an important reference and a highly useful resource for researchers and practitioners in statistics, economics, business, engineering, and environmental analysis. An Instructor's Manual presenting detailed solutions to all the problems in he book is available upon request from the Wiley editorial department.
R 8,335.00
Missing Data Analysis in Practice
Missing Data Analysis in Practice provides practical methods for analyzing missing data along with the heuristic reasoning for understanding the theoretical underpinnings. Drawing on his 25 years of experience researching, teaching, and consulting in quantitative areas, the author presents both frequentist and Bayesian perspectives. He describes easy-to-implement approaches, the underlying assumptions, and practical means for assessing these assumptions. Actual and simulated data sets illustrate important concepts, with the data sets and codes available online.The book underscores the development of missing data methods and their adaptation to practical problems. It mainly focuses on the traditional missing data problem. The author also shows how to use the missing data framework in many other statistical problems, such as measurement error, finite population inference, disclosure limitation, combing information from multiple data sources, and causal inference.
R 5,633.63
Evaluation Essentials, Third Edition
Beloved for its conversational style and reliable advice, this text is now in a revised and updated third edition, reflecting key developments in evaluation. It includes expanded coverage of equity and social justice issues, values and cost analysis, visualizing qualitative data with software, and more. Twenty-six concise chapters, or "sessions," give students, applied researchers, and program administrators a solid foundation for conducting or using evaluations. Covering both quantitative and qualitative methods, the book emphasizes fostering evaluation use. It shows how to build collaborative relationships with users; formulate answerable evaluation questions; deal with contingencies that might alter the traditional sequence of an evaluation; and collect, analyze, and report data. Student-friendly features throughout the sessions include titles written as questions, bulleted recaps, "Thinking Ahead" and "Next Steps" pointers, cautionary notes, and annotated suggestions for further reading. An in-depth case study provides the basis for end-of-session practice exercises. New to This Edition *New sessions on context-sensitive evaluation, including the organizational, sociopolitical, and community contexts of a program. *New or expanded discussions of timely topics: identifying evaluation decision makers, analyzing program costs, coding and visualizing qualitative data with software, and more. *Updated suggestions for further reading and discussion in every chapter.
R 3,210.00
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
Bioimage Data Analysis Workflows ‒ Advanced Components and Methods
This open access textbook aims at providing detailed explanations on how to design and construct image analysis workflows to successfully conduct bioimage analysis. Addressing the main challenges in image data analysis, where acquisition by powerful imaging devices results in very large amounts of collected image data, the book discusses techniques relying on batch and GPU programming, as well as on powerful deep learning-based algorithms. In addition, downstream data processing techniques are introduced, such as Python libraries for data organization, plotting, and visualizations. Finally, by studying the way individual unique ideas are implemented in the workflows, readers are carefully guided through how the parameters driving biological systems are revealed by analyzing image data. These studies include segmentation of plant tissue epidermis, analysis of the spatial pattern of the eye development in fruit flies, and the analysis of collective cell migration dynamics. The presented content extends the Bioimage Data Analysis Workflows textbook (Miura, Sladoje, 2020), published in this same series, with new contributions and advanced material, while preserving the well-appreciated pedagogical approach adopted and promoted during the training schools for bioimage analysis organized within NEUBIAS – the Network of European Bioimage Analysts. This textbook is intended for advanced students in various fields of the life sciences and biomedicine, as well as staff scientists and faculty members who conduct regular quantitative analyses of microscopy images.
R 1,832.00
Genomics Data Analysis
Statisticians have met the need to test hundreds or thousands of genomics hypotheses simultaneously with novel empirical Bayes methods that combine advantages of traditional Bayesian and frequentist statistics. Techniques for estimating the local false discovery rate assign probabilities of differential gene expression, genetic association, etc. without requiring subjective prior distributions. This book brings these methods to scientists while keeping the mathematics at an elementary level. Readers will learn the fundamental concepts behind local false discovery rates, preparing them to analyze their own genomics data and to critically evaluate published genomics research.Key Features:* dice games and exercises, including one using interactive software, for teaching the concepts in the classroom* examples focusing on gene expression and on genetic association data and briefly covering metabolomics data and proteomics data* gradual introduction to the mathematical equations needed* how to choose between different methods of multiple hypothesis testing* how to convert the output of genomics hypothesis testing software to estimates of local false discovery rates* guidance through the minefield of current criticisms of p values* material on non-Bayesian prior p values and posterior p values not previously published
R 1,246.00
R 1,527.00
Stochastic Analysis and Applications
This volume attempts to exhibit current research in stochastic integration, stochastic differential equations, stochastic optimization and stochastic problems in physics and biology. It includes information on the theory of Dirichlet forms, Feynman integration and the Schrodinger's equation.
R 3,737.84
Data Analysis Techniques for Physical Scientists
A comprehensive guide to data analysis techniques for physical scientists, providing a valuable resource for advanced undergraduate and graduate students, as well as seasoned researchers. The book begins with an extensive discussion of the foundational concepts and methods of probability and statistics under both the frequentist and Bayesian interpretations of probability. It next presents basic concepts and techniques used for measurements of particle production cross-sections, correlation functions, and particle identification. Much attention is devoted to notions of statistical and systematic errors, beginning with intuitive discussions and progressively introducing the more formal concepts of confidence intervals, credible range, and hypothesis testing. The book also includes an in-depth discussion of the methods used to unfold or correct data for instrumental effects associated with measurement and process noise as well as particle and event losses, before ending with a presentation of elementary Monte Carlo techniques.
R 4,527.86
Biostatistical Design and Analysis Using R
R — the statistical and graphical environment is rapidly emerging as an important set of teaching and research tools for biologists. This book draws upon the popularity and free availability of R to couple the theory and practice of biostatistics into a single treatment, so as to provide a textbook for biologists learning statistics, R, or both. An abridged description of biostatistical principles and analysis sequence keys are combined together with worked examples of the practical use of R into a complete practical guide to designing and analyzing real biological research. Topics covered include: simple hypothesis testing, graphingexploratory data analysis and graphical summariesregression (linear, multi and non-linear)simple and complex ANOVA and ANCOVA designs (including nested, factorial, blocking, spit-plot and repeated measures)frequency analysis and generalized linear models. Linear mixed effects modeling is also incorporated extensively throughout as an alternative to traditional modeling techniques. The book is accompanied by a companion website www.wiley.com/go/logan/r with an extensive set of resources comprising all R scripts and data sets used in the book, additional worked examples, the biology package, and other instructional materials and links.
R 2,962.00
Analysis of Doubly Truncated Data
This book introduces readers to statistical methodologies used to analyze doubly truncated data. The first book exclusively dedicated to the topic, it provides likelihood-based methods, Bayesian methods, non-parametric methods, and linear regression methods. These procedures can be used to effectively analyze continuous data, especially survival data arising in biostatistics and economics. Because truncation is a phenomenon that is often encountered in non-experimental studies, the methods presented here can be applied to many branches of science. The book provides R codes for most of the statistical methods, to help readers analyze their data. Given its scope, the book is ideally suited as a textbook for students of statistics, mathematics, econometrics, and other fields.
R 2,015.00
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
Biostatistics – A Foundation for Analysis in the Health Sciences, 10e Student Solutions Manual
Solutions and explanations for problems in Biostatistics Biostatistics: A Foundation for Analysis in the Health Sciences, 10th Edition -- Student Solutions Manual offers complete solutions to the odd-numbered practice problems in the text. Each answer includes all graphs and tables as required, and detailed explanations accompany more complex answers as needed. Biostatistics problems can become complicated very quickly, and practice is the only way to master some of the more difficult scenarios. By helping you see just where you went wrong, and providing the reasoning behind the correct answer, this solutions manual helps you study more effectively and retain vital information.
R 2,147.00
Occupational and Activity Analysis
Newly updated to reflect the Occupational Therapy Practice Framework: Domain and Process, Fourth Edition (OTPF-4), Occupational and Activity Analysis, Third Edition outlines the process of conducting occupational and activity analyses for occupational therapy students and clinicians.Occupational therapy practitioners use occupations and activities not only as a goal but also as a treatment medium, so understanding both the uniqueness of a client’s occupations and how an activity can be used therapeutically is essential. This text is an introduction to both realms, first by explaining the process by which to peel back the layers of an occupation or activity to reveal its intricacy and then examining how to use this information for evaluation and intervention.Dr. Heather Thomas has updated Occupational and Activity Analysis, Third Edition to reflect the significant changes made to the activity analysis process and terminology in the OTPF-4. Conducting either an occupational or activity analysis investigates not only what is required for full participation but also looks at the meaning ascribed to it by the people, groups, or communities engaging in it and how personal and environmental contexts impact participation.What’s new and included in the Third Edition:Chapters throughout the text have been updated to reflect the changes in the OTPF-4.Updated educational standards set by the Accreditation Council for Occupational Therapy Education (ACOTE) for doctoral and master’s level and occupational therapy assistant programs.Additional cases and relevant clinical examples.Updated tables, boxes, and figures throughout.Expanded section on communication management.Appendices containing updated occupational and activity analysis forms.Example of a full activity analysis.Updated and added photos to help students understand concepts.Instructors in educational settings can visit www.efacultylounge.com for forms, videos, and PowerPoint presentations to be used for teaching in the classroom.As a foundational skill, occupational and activity analysis is utilized throughout students’ careers and into their lives as practitioners, making Occupational and Activity Analysis, Third Edition the perfect textbook for the occupational therapy or occupational therapy assistant student, faculty, or clinician.
R 3,757.73
Handbook of Mixture Analysis
Mixture models have been around for over 150 years, and they are found in many branches of statistical modelling, as a versatile and multifaceted tool. They can be applied to a wide range of data: univariate or multivariate, continuous or categorical, cross-sectional, time series, networks, and much more. Mixture analysis is a very active research topic in statistics and machine learning, with new developments in methodology and applications taking place all the time. The Handbook of Mixture Analysis is a very timely publication, presenting a broad overview of the methods and applications of this important field of research. It covers a wide array of topics, including the EM algorithm, Bayesian mixture models, model-based clustering, high-dimensional data, hidden Markov models, and applications in finance, genomics, and astronomy.Features:Provides a comprehensive overview of the methods and applications of mixture modelling and analysisDivided into three parts: Foundations and Methods; Mixture Modelling and Extensions; and Selected ApplicationsContains many worked examples using real data, together with computational implementation, to illustrate the methods describedIncludes contributions from the leading researchers in the fieldThe Handbook of Mixture Analysis is targeted at graduate students and young researchers new to the field. It will also be an important reference for anyone working in this field, whether they are developing new methodology, or applying the models to real scientific problems.
R 3,790.36
R 565.35
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 1,224.39
Machine Learning Methods for Behaviour Analysis and Anomaly Detection in Video
This thesis proposes machine learning methods for understanding scenes via behaviour analysis and online anomaly detection in video. The book introduces novel Bayesian topic models for detection of events that are different from typical activities and a novel framework for change point detection for identifying sudden behavioural changes.Behaviour analysis and anomaly detection are key components of intelligent vision systems. Anomaly detection can be considered from two perspectives: abnormal events can be defined as those that violate typical activities or as a sudden change in behaviour. Topic modelling and change-point detection methodologies, respectively, are employed to achieve these objectives.The thesis starts with the development of learning algorithms for a dynamic topic model, which extract topics that represent typical activities of a scene. These typical activities are used in a normality measure in anomaly detection decision-making. The book also proposes a novel anomaly localisation procedure. In the first topic model presented, a number of topics should be specified in advance. A novel dynamic nonparametric hierarchical Dirichlet process topic model is then developed where the number of topics is determined from data. Batch and online inference algorithms are developed.The latter part of the thesis considers behaviour analysis and anomaly detection within the change-point detection methodology. A novel general framework for change-point detection is introduced. Gaussian process time series data is considered. Statistical hypothesis tests are proposed for both offline and online data processing and multiple change point detection are proposed and theoretical properties of the tests are derived. The thesis is accompanied by open-source toolboxes that can be used by researchers and engineers.
R 3,339.80
SPSS Companion for the Third Edition of The Fundamentals of Political Science Research
An SPSS Companion for the Third Edition of The Fundamentals of Political Science Research offers students a chance to delve into the world of SPSS using real political science data sets and statistical analysis techniques directly from Paul M. Kellstedt and Guy D. Whitten's best-selling textbook. Built in parallel with the main text, this workbook teaches students to apply the techniques they learn in each chapter by reproducing the analyses and results from each lesson using SPSS. Students will also learn to create all of the tables and figures found in the textbook, leading to an even greater mastery of the core material. This accessible, informative, and engaging companion walks through the use of SPSS step-by-step, using command lines and screenshots to demonstrate proper use of the software. With the help of these guides, students will become comfortable creating, editing, and using data sets in SPSS to produce original statistical analyses for evaluating causal claims. End-of-chapter exercises encourage this innovation by asking students to formulate and evaluate their own hypotheses.
R 935.54
Experimental Design and Data Analysis for Biologists
Applying statistical concepts to biological scenarios, this established textbook continues to be the go-to tool for advanced undergraduates and postgraduates studying biostatistics or experimental design in biology-related areas. Chapters cover linear models, common regression and ANOVA methods, mixed effects models, model selection, and multivariate methods used by biologists, requiring only introductory statistics and basic mathematics. Demystifying statistical concepts with clear, jargon-free explanations, this new edition takes a holistic approach to help students understand the relationship between statistics and experimental design. Each chapter contains further-reading recommendations, and worked examples from today's biological literature. All examples reflect modern settings, methodology and equipment, representing a wide range of biological research areas. These are supported by hands-on online resources including real-world data sets, full R code to help repeat analyses for all worked examples, and additional review questions and exercises for each chapter.
R 2,437.00
Introduction to Computer-Intensive Methods of Data Analysis in Biology
This 2006 guide to the contemporary toolbox of methods for data analysis will serve graduate students and researchers across the biological sciences. Modern computational tools, such as Maximum Likelihood, Monte Carlo and Bayesian methods, mean that data analysis no longer depends on elaborate assumptions designed to make analytical approaches tractable. These new 'computer-intensive' methods are currently not consistently available in statistical software packages and often require more detailed instructions. The purpose of this book therefore is to introduce some of the most common of these methods by providing a relatively simple description of the techniques. Examples of their application are provided throughout, using real data taken from a wide range of biological research. A series of software instructions for the statistical software package S-PLUS are provided along with problems and solutions for each chapter.
R 6,644.64
Stochastic Analysis for Finance with Simulations
This book is an introduction to stochastic analysis and quantitative finance; it includes both theoretical and computational methods. Topics covered are stochastic calculus, option pricing, optimal portfolio investment, and interest rate models. Also included are simulations of stochastic phenomena, numerical solutions of the Black–Scholes–Merton equation, Monte Carlo methods, and time series. Basic measure theory is used as a tool to describe probabilistic phenomena. The level of familiarity with computer programming is kept to a minimum. To make the book accessible to a wider audience, some background mathematical facts are included in the first part of the book and also in the appendices. This work attempts to bridge the gap between mathematics and finance by using diagrams, graphs and simulations in addition to rigorous theoretical exposition. Simulations are not only used as the computational method in quantitative finance, but they can also facilitate an intuitive and deeper understanding of theoretical concepts. Stochastic Analysis for Finance with Simulations is designed for readers who want to have a deeper understanding of the delicate theory of quantitative finance by doing computer simulations in addition to theoretical study. It will particularly appeal to advanced undergraduate and graduate students in mathematics and business, but not excluding practitioners in finance industry.
R 3,297.00
