Mathematical and statistical software Books

Book SynopsisAspects of environmental change are some of the greatest challenges faced by policymakers today. The key issues addressed by environmental science are often empirical, and in many instances very detailed, sizable datasets are available. Researchers in this field should have a solid understanding of the econometric tools best suited for analysis of these data. While complex and expensive physical models of the environment exist, it is becoming increasingly clear that reduced-form econometric models have an important role to play in modeling environmental phenomena. In short, successful environmental modeling does not necessarily require a structural model, but the econometric methods underlying a reduced-form approach must be competently executed. Environmental Econometrics Using Stata provides an important starting point for this journey by presenting a broad range of applied econometric techniques for environmental econometrics and illustrating how they can be applied in Stata. The emphasis is not only on how to formulate and fit models in Stata but also on the need to use a wide range of diagnostic tests in order to validate the results of estimation and subsequent policy conclusions. This focus on careful, reproducible research should be appreciated by academic and non-academic researchers who are seeking to produce credible, defensible conclusions about key issues in environmental science. Table of Contents1 Introduction 2 Linear regression models 3 Beyond ordinary least squares 4 Introducing dynamics 5 Multivariate time-series models 6 Testing for nonstationarity 7 Modeling nonstationary variables 8 Forecasting 9 Structural time-series models 10 Nonlinear time-series models 11 Modeling time-varying variance 12 Longitudinal data models 13 Spatial models 14 Discrete dependent variables 15 Fractional integration A Using Stata

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Book SynopsisThis book presents strategies for analyzing qualitative and mixed methods data with MAXQDA software, and provides guidance on implementing a variety of research methods and approaches, e.g. grounded theory, discourse analysis and qualitative content analysis, using the software. In addition, it explains specific topics, such as transcription, building a coding frame, visualization, analysis of videos, concept maps, group comparisons and the creation of literature reviews. The book is intended for masters and PhD students as well as researchers and practitioners dealing with qualitative data in various disciplines, including the educational and social sciences, psychology, public health, business or economics.Table of ContentsIntroduction: Analyzing Qualitative Data with Software.- Getting to Know the Interface of MAXQDA.- Setting up a Project and Importing Data.- Transcribing Audio and Video Recordings.- Exploring the Data.- Coding Text and PDF Files.- Coding Video Data, Audio Data, and Images.- Building a Coding Frame.- Working with Coded Segments and Memos.- Adding Variables and Quantifying Codes.- Working with Paraphrases and Summaries, Creating Case Overviews.- Comparing Cases and Groups, Discovering Interrelations and Using Visualizations.- Analyzing Mixed Methods Data.- Working with Bibliographic Information and Creating Literature Reviews.- Analyzing Focus Group Data.- Analyzing (Online) Survey Data with Closed and Open-Ended Questions.- MAXMaps: Creating Infographics and Concept Maps.- Collaborating in Teams.- Analyzing Intercoder Agreement.- Documenting and Archiving the Research Process.

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Book SynopsisThe new edition of this book provides an easily accessible introduction to the statistical analysis of network data using R. It has been fully revised and can be used as a stand-alone resource in which multiple R packages are used to illustrate how to conduct a wide range of network analyses, from basic manipulation and visualization, to summary and characterization, to modeling of network data. The central package is igraph, which provides extensive capabilities for studying network graphs in R. The new edition of this book includes an overhaul to recent changes in igraph. The material in this book is organized to flow from descriptive statistical methods to topics centered on modeling and inference with networks, with the latter separated into two sub-areas, corresponding first to the modeling and inference of networks themselves, and then, to processes on networks. The book begins by covering tools for the manipulation of network data. Next, it addresses visualization and characterization of networks. The book then examines mathematical and statistical network modeling. This is followed by a special case of network modeling wherein the network topology must be inferred. Network processes, both static and dynamic are addressed in the subsequent chapters. The book concludes by featuring chapters on network flows, dynamic networks, and networked experiments. Statistical Analysis of Network Data with R, 2nd Ed. has been written at a level aimed at graduate students and researchers in quantitative disciplines engaged in the statistical analysis of network data, although advanced undergraduates already comfortable with R should find the book fairly accessible as well.Table of Contents1 Introduction.- 2 Manipulating Network Data.- 3 Visualizing Network Data.- 4 Descriptive Analysis of Network Graph Characteristics.- 5 Mathematical Models for Network Graphs.- 6 Statistical Models for Network Graphs.- 7 Network Topology Inference.- 8 Modeling and Prediction for Processes on Network Graphs.- 9 Analysis of Network Flow Data.- 10 Networked Experiments.- 11 Dynamic Networks.- Index.

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Book SynopsisPartial least squares structural equation modeling (PLS-SEM) has become a standard approach for analyzing complex inter-relationships between observed and latent variables. Researchers appreciate the many advantages of PLS-SEM such as the possibility to estimate very complex models and the method’s flexibility in terms of data requirements and measurement specification. This practical open access guide provides a step-by-step treatment of the major choices in analyzing PLS path models using R, a free software environment for statistical computing, which runs on Windows, macOS, and UNIX computer platforms. Adopting the R software’s SEMinR package, which brings a friendly syntax to creating and estimating structural equation models, each chapter offers a concise overview of relevant topics and metrics, followed by an in-depth description of a case study. Simple instructions give readers the “how-tos” of using SEMinR to obtain solutions and document their results. Rules of thumb in every chapter provide guidance on best practices in the application and interpretation of PLS-SEM.Table of ContentsAn Introduction to Structural Equation Modeling.- Introduction to R and RStudio.- Introduction to SEMinR.- Evaluation of Reflective Measurement Models.- Evaluation of Formative Measurement Models.- Evaluation of the Structural Model.- Mediation Analysis.- Moderation Analysis.

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Book SynopsisMATLAB and Simulink Crash Course for Engineers is a reader-friendly introductory guide to the features, functions, and applications of MATLAB and Simulink. The book provides readers with real-world examples, exercises, and applications, and offers highly illustrated, step-by-step demonstrations of techniques for the modelling and simulation of complex systems. MATLAB coverage includes vectors and matrices, programs and functions, complex numbers, visualization, solving equations, numerical methods, optimization problems, and graphical user interfaces. The Simulink coverage includes commonly used Simulink blocks, control system simulation, electrical circuit analysis, electric power systems, power electronics, and renewable energy technology. This powerful tutorial is a great resource for students, engineers, and other busy technical professionals who need to quickly acquire a solid understanding of MATLAB and Simulink.Table of ContentsIntroduction to MATLAB.- Vectors and Matrices.- Programs and Functions.- Complex Numbers.- Visualization.- Solving Equations.- Numerical Methods in MATLAB.- Electrical Circuit Analysis.- Control System and MATLAB.- Optimization Problem.- App Designer and Graphical User Interface in MATLAB.- Introduction to Simulink.- Control System in Simulink.- Commonly Used Simulink Blocks.- Electrical Circuit Analysis in Simulink.- Application of Simulink in Power Systems.- Application of Simulink in Power Electronics.- Application of Simulink in Renewable Energy Technology.

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Book SynopsisThis book aims to provide an advanced R software approach that can carry out rainfall trend analysis using Mann-Kendall and Sen’s slope estimator tests. The research study follows a systematic approach while utilizing R software as it can greatly facilitate the analysis of rainfall trends. About 30 stations located in the study area and 41 to 50 years’ time series were selected for the purpose of analysis. The data for the research was collected from the State Water Data Centre (SWDC) in Gujarat, Indian Meteorological Department (IMD) in Pune, DAAC (NASA), and ESRI. Cluster analysis has been performed to analyze the variability of the mean rainfall. The stations have been divided into 2 clusters with 17 and 13 stations in each cluster which significantly differ from each other. This book is aimed at researchers, scientists and government organizations working in the field of climate change. Table of ContentsChapter 1. Introduction.- Chapter 2. Literature survey.- Chapter 3. Study area and data collection.- Chapter 4. Methodology.- Chapter 5. Computations.- Chapter 6. Results and discussion.- Chapter 7. Conclusion.

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Book SynopsisGain full insights about data handling and data visualization in MATLAB and Python. Knowledge about signal and image processing in MATLAB and Python. Apply coding skills to solve real-world problems. Selection of right language for specific coding tasks.

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Book SynopsisAn Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical sTable of ContentsPreface.- 1 Introduction.- 2 Statistical Learning.- 3 Linear Regression.- 4 Classification.- 5 Resampling Methods.- 6 Linear Model Selection and Regularization.- 7 Moving Beyond Linearity.- 8 Tree-Based Methods.- 9 Support Vector Machines.- 10 Deep Learning.- 11 Survival Analysis and Censored Data.- 12 Unsupervised Learning.- 13 Multiple Testing.- Index.

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Book SynopsisGet going with tidymodels, a collection of R packages for modeling and machine learning. Whether you're just starting out or have years of experience with modeling, this practical introduction shows data analysts, business analysts, and data scientists how the tidymodels framework offers a consistent, flexible approach for your work.

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Book SynopsisTable of ContentsForeword xi About the Companion Website xiii 1 Data Cleaning 1 1.1 The Statistical Value Chain 1 1.1.1 Raw Data 2 1.1.2 Input Data 2 1.1.3 Valid Data 3 1.1.4 Statistics 3 1.1.5 Output 3 1.2 Notation and Conventions Used in this Book 3 2 A Brief Introduction to R 5 2.1 R on the Command Line 5 2.1.1 Getting Help and Learning R 6 2.2 Vectors 7 2.2.1 Computing with Vectors 9 2.2.2 Arrays and Matrices 10 2.3 Data Frames 11 2.3.1 The Formula-Data Interface 12 2.3.2 Selecting Rows and Columns; Boolean Operators 12 2.3.3 Selection with Indices 13 2.3.4 Data Frame Manipulation:The dplyr Package 14 2.4 Special Values 15 2.4.1 Missing Values 17 2.5 Getting Data into and out of R 18 2.5.1 File Paths in R 19 2.5.2 Formats Provided by Packages 20 2.5.3 Reading Data from a Database 20 2.5.4 Working with Data External to R 21 2.6 Functions 21 2.6.1 Using Functions 22 2.6.2 Writing Functions 22 2.7 Packages Used in this Book 23 3 Technical Representation of Data 27 3.1 Numeric Data 28 3.1.1 Integers 28 3.1.2 Integers in R 30 3.1.3 Real Numbers 31 3.1.4 Double Precision Numbers 31 3.1.5 The Concept of Machine Precision 33 3.1.6 Consequences ofWorking with Floating Point Numbers 34 3.1.7 Dealing with the Consequences 35 3.1.8 Numeric Data in R 37 3.2 Text Data 38 3.2.1 Terminology and Encodings 38 3.2.2 Unicode 39 3.2.3 Some Popular Encodings 40 3.2.4 Textual Data in R: Objects of Class Character 43 3.2.5 Encoding in R 44 3.2.6 Reading andWriting of Data with Non-Local Encoding 46 3.2.7 Detecting Encoding 48 3.2.8 Collation and Sorting 49 3.3 Times and Dates 50 3.3.1 AIT, UTC, and POSIX Seconds Since the Epcoch 50 3.3.2 Time and Date Notation 52 3.3.3 Time and Date Storage in R 54 3.3.4 Time and Date Conversion in R 55 3.3.5 Leap Days, Time Zones, and Daylight Saving Times 57 3.4 Notes on Locale Settings 58 4 Data Structure 61 4.1 Introduction 61 4.2 Tabular Data 61 4.2.1 data.frame 61 4.2.2 Databases 62 4.2.3 dplyr 64 4.3 Matrix Data 65 4.4 Time Series 66 4.5 Graph Data 68 4.6 Web Data 69 4.6.1 Web Scraping 69 4.6.2 Web API 70 4.7 Other Data 72 4.8 Tidying Tabular Data 72 4.8.1 Variable Per Column 74 4.8.2 Single Observation Stored in Multiple Tables 75 5 Cleaning Text Data 77 5.1 Character Normalization 78 5.1.1 Encoding Conversion and Unicode Normalization 78 5.1.2 Character Conversion and Transliteration 80 5.2 Pattern Matching with Regular Expressions 81 5.2.1 Basic Regular Expressions 82 5.2.2 Practical Regular Expressions 85 5.2.3 Generating Regular Expressions in R 92 5.3 Common String Processing Tasks in R 93 5.4 Approximate Text Matching 98 5.4.1 String Metrics 100 5.4.2 String Metrics and Approximate Text Matching in R 109 6 Data Validation 119 6.1 Introduction 119 6.2 A First Look at the validate Package 120 6.2.1 Quick Checks with check_that 120 6.2.2 The BasicWorkflow: validator and confront 122 6.2.3 A Little Background on validate and DSLs 124 6.3 Defining Data Validation 125 6.3.1 Formal Definition of Data Validation 126 6.3.2 Operations on Validation Functions 128 6.3.3 Validation and Missing Values 130 6.3.4 Structure of Validation Functions 131 6.3.5 Demarcating Validation Rules in validate 132 6.4 A Formal Typology of Data Validation Functions 134 6.4.1 A Closer Look at Measurement 134 6.4.2 Classification of Validation Rules 135 6.5 Validating Data with the validate Package 137 6.5.1 Validation Rules in the Console and the validator Object 137 6.5.2 Validating in the Pipeline 139 6.5.3 Raising Errors orWarnings 140 6.5.4 Tolerance for Testing Linear Equalities 140 6.5.5 Setting and Resetting Options 141 6.5.6 Importing and Exporting Validation Rules from and to File 142 6.5.7 Checking Variable Types and Metadata 145 6.5.8 Checking Value Ranges and Code Lists 146 6.5.9 Checking In-Record Consistency Rules 146 6.5.10 Checking Cross-Record Validation Rules 148 6.5.11 Checking Functional Dependencies 149 6.5.12 Cross-Dataset Validation 150 6.5.13 Macros, Variable Groups, Keys 152 6.5.14 Analyzing Output: validation Objects 152 6.5.15 Output Dimensionality and Output Selection 155 7 Localizing Errors in Data Records 157 7.1 Error Localization 157 7.2 Error Localization with R 160 7.2.1 The Errorlocate Package 160 7.3 Error Localization as MIP-Problem 163 7.3.1 Error Localization and Mixed-Integer Programming 163 7.3.2 Linear Restrictions 164 7.3.3 Categorical Restrictions 165 7.3.4 Mixed-Type Restrictions 167 7.4 Numerical Stability Issues 170 7.4.1 A Short Overview of MIP Solving 170 7.4.2 Scaling Numerical Records 172 7.4.3 Setting NumericalThreshold Values 173 7.5 Practical Issues 174 7.5.1 Setting ReliabilityWeights 174 7.5.2 Simplifying Conditional Validation Rules 176 7.6 Conclusion 180 8 Rule Set Maintenance and Simplification 183 8.1 Quality of Validation Rules 183 8.1.1 Completeness 183 8.1.2 Superfluous Rules and Infeasibility 184 8.2 Rules in the Language of Logic 184 8.2.1 Using Logic to Rewrite Rules 185 8.3 Rule Set Issues 186 8.3.1 Infeasible Rule Set 186 8.3.2 Fixed Value 187 8.3.3 Redundant Rule 188 8.3.4 Nonrelaxing Clause 189 8.3.5 Nonconstraining Clause 189 8.4 Detection and Simplification Procedure 190 8.4.1 Mixed-Integer Programming 190 8.4.2 Detecting Feasibility 191 8.4.3 Finding Rules Causing Infeasibility 191 8.4.4 Detecting Conflicting Rules 191 8.4.5 Detect Partial Infeasibility 192 8.4.6 Detect Fixed Values 192 8.4.7 Detect Nonrelaxing Clauses 192 8.4.8 Detecting Nonconstraining Clauses 193 8.4.9 Detecting Redundant Rules 193 8.5 Conclusion 194 9 Methods Based on Models for Domain Knowledge 195 9.1 Correction with Data Modifying Rules 195 9.1.1 Modifying Functions 196 9.1.2 A Class of Modifying Functions on Numerical Data 201 9.2 Rule-Based Correction with dcmodify 205 9.2.1 Reading Rules from File 206 9.2.2 Modifying Rule Syntax 207 9.2.3 Missing Values 208 9.2.4 Sequential and Sequence-Independent Execution 208 9.2.5 Options Settings Management 209 9.3 Deductive Correction 209 9.3.1 Correcting Typing Errors in Numeric Data 209 9.3.2 Deductive Imputation Using Linear Restrictions 213 10 Imputation and Adjustment 219 10.1 Missing Data 219 10.1.1 Missing Data Mechanisms 219 10.1.2 Visualizing and Testing for Patterns in Missing Data Using R 220 10.2 Model-Based Imputation 224 10.3 Model-Based Imputation in R 226 10.3.1 Specifying ImputationMethods with simputation 226 10.3.2 Linear Regression-Based Imputation 227 10.3.3 M-Estimation 230 10.3.4 Lasso, Ridge, and Elasticnet Regression 231 10.3.5 Classification and Regression Trees 232 10.3.6 Random Forest 235 10.4 Donor Imputation with R 236 10.4.1 Random and Sequential Hot Deck Imputation 237 10.4.2 k Nearest Neighbors and Predictive Mean Matching 238 10.5 Other Methods in the simputation Package 239 10.6 Imputation Based on the EM Algorithm 240 10.6.1 The EM Algorithm 241 10.6.2 EM Imputation Assuming the Multivariate Normal Distribution 243 10.7 Sampling Variance under Imputation 244 10.8 Multiple Imputations 246 10.8.1 Multiple Imputation Based on the EM Algorithm 248 10.8.2 The Amelia Package 249 10.8.3 Multivariate Imputation with Chained Equations (Mice) 252 10.8.4 Imputation with the mice Package 254 10.9 Analytic Approaches to Estimate Variance of Imputation 256 10.9.1 Imputation as Part of the Estimator 256 10.10 Choosing an ImputationMethod 257 10.11 Constraint Value Adjustment 259 10.11.1 Formal Description 259 10.11.2 Application to Imputed Data 262 10.11.3 Adjusting Imputed Values with the rspa Package 263 11 Example: A Small Data-Cleaning System 265 11.1 Setup 266 11.1.1 DeterministicMethods 266 11.1.2 Error Localization 269 11.1.3 Imputation 269 11.1.4 Adjusting Imputed Data 271 11.2 Monitoring Changes in Data 273 11.2.1 Data Diff (Daff) 274 11.2.2 Summarizing Cell Changes 275 11.2.3 Summarizing Changes in Conformance to Validation Rules 277 11.2.4 Track Changes in Data Automatically with lumberjack 278 11.3 Integration and Automation 282 11.3.1 Using RScript 283 11.3.2 The docopt Package 283 11.3.3 Automated Data Cleaning 285 References 287 Index 297

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Book SynopsisTransform your organization''s data into actionable insights with Tableau Tableau is designed specifically to provide fast and easy visual analytics. The intuitive drag-and-drop interface helps you create interactive reports, dashboards, and visualizations, all without any special or advanced training. This all new edition of Tableau Your Data! is your Tableau companion, helping you get the most out of this invaluable business toolset.Tableau Your Data! shows you how to build dynamic, best of breed visualizations using the Tableau Software toolset. This comprehensive guide covers the core feature set for data analytics, and provides clear step-by-step guidance toward best practices and advanced techniques that go way beyond the user manual. You''ll learn how Tableau is different from traditional business information analysis tools, and how to navigate your way around the Tableau 9.0 desktop before delving into functions and calculations, as well as sTable of ContentsIntroduction xxv Part I Desktop 1 1 Creating Visual Analytics with Tableau Desktop 3 The Shortcomings of Traditional Information Analysis 4 The Business Case for Visual Analysis 5 Three Kinds of Data That Exist in Every Entity 5 How Visual Analytics Improves Decision Making 6 Turning Data into Information with Visual Analytics 8 Analysis as a Creative Process 8 Tableau’s Desktop Tools 9 Tableau Desktop Personal Edition 9 Professional Edition 9 Tableau File Types 9 Tableau Reader 11 Tableau Online Help 11 Introducing the Tableau Desktop Workspace 11 New Workspace Design 11 Using the Start Page Controls Effectively 12 The Start Page 12 The Tableau Desktop Workspace 17 Summary 41 2 Connecting to Your Data 43 What You Will Learn in This Chapter 43 How to Connect to Your Data 44 Connecting to Desktop Sources 45 Understanding the Data Source Page 47 What Are Generated Values? 57 Knowing When to Use a Direct Connection or a Data Extract 61 Using Tableau’s File Types Effectively 63 Dealing with Data Shaping and Data Quality 65 The Data Interpreter 68 3 Building Your First Visualization 93 Fast and Easy Analysis via Show Me 93 New Features 94 How Show Me Works 94 The Analytics Pane 103 Sorting Data in Tableau 118 Enhancing View with Filters, Sets, Groups, and Hierarchies 121 How Tableau Uses Date Fields 143 4 Creating Calculations to Enhance Data 155 What Is Aggregation? 156 Dimension versus Attribute 157 What Are Calculated Fields and Table Calculations? 159 How Do Calculated Fields Work? 159 Creating Calculated Fields with the Calculation Editor 160 Performing Ad Hoc Calculations 161 How Do Table Calculations Work? 161 A Word on Calculations and Cubes 162 Using the Calculation Editor to Build Calculated Fields 163 Ad Hoc Calculated Fields 164 Building Formulas Using Table Calculations 166 Adding Flexibility to Calculations with Parameters 177 Why You Should Learn Level of Detail Expressions 183 5 Using Maps to Improve Insight 191 New Map Features 192 Creating a Standard Map View 192 How Tableau Geocodes Your Data 195 Searching for Items in Maps 197 Typical Map Errors and How to Deal with Them 199 Plotting Your Own Locations on a Map 200 Replacing Tableau’s Standard Maps 205 Using Custom Background Images to Plot Spatial Data 211 Notes 219 6 Developing an Ad Hoc Analysis Environment 221 Data Discovery as a Creative Process 221 Preparing Your Team for Success 222 Qualities of a Good Data Analyst 223 Doing Effective Discovery Work 224 What IT Can Do to Help 224 Spreading Discovery to Information Consumers 225 Generating New Data with Forecasts 225 Providing Self-Service Ad Hoc Analysis with Parameters 231 What Are Parameters? 231 How Can Parameters Be Used? 231 Basic Parameter Controls 232 Advanced Parameter Controls 236 Editing Views in Tableau Server 239 7 Tips, Tricks, and Timesavers 243 Saving Time and Improving Formatting 243 Double-Click Fields to Build Faster 243 Reduce Clicks Using the Right Mouse Button Drag 245 Quick Copy Fields with Control-Drag 246 Replace Fields by Dropping the New Field on Top 246 Right-Click to Edit or Format Anything 247 Editing or Removing Titles from Axis Headings 247 Quicken Your Presentation Page Views 248 A Faster Way to Access Field Menu Options 250 Zooming the Formula Dialog Box 250 Drag a Field into the Formula Dialog box 250 Swap Data in Pane and Reference Line Fields 251 Improving Appearance to Convey Meaning More Precisely 251 Changing the Appearance of Dates 251 Formatting Tooltip Content 252 Change the Order of Color Expressed in Charts 252 Exposing a Header in a One-Column Text Table 253 Unpacking a Packaged Workbook File 255 Make a Parameterized Axis Label 255 Using Continuous Quick Filters for Ranges of Values 256 Create Your Own Custom Date Hierarchy 256 Concatenating to Make Custom Fields 258 Using Legends to Build Highlight Actions 258 Formatting Null Value Results 260 When to Use Floating Objects in Dashboards 264 Combined Axis Shading in a Scatter Plot 266 Creating Folders to Hold Fields 268 Customizing Shapes, Colors, Fonts, and Images 269 Customizing Shapes 269 Customizing Colors 271 Customizing Fonts 272 Customizing Images in Dashboards 273 Advanced Chart Types 274 Bar-in-Bar Chart 274 Pareto Charts 275 Sparklines 280 Bullet Graphs 281 8 Bringing It All Together with Dashboards 285 How Dashboards Facilitate Analysis and Understanding 285 How Tableau Improves the Dashboard-Building Process 286 The Wrong Way to Build a Dashboard 287 The Right Way to Build a Dashboard 289 Best Practices for Dashboard Building 290 Size the Dashboard to Fit the Worst-Case Available Space 291 Employ Four-Pane Dashboard Designs 291 Use Actions to Filter Instead of Quick Filters 293 Build Cascading Dashboard Designs to Improve Load Speeds 293 Limit the Use of Color to One Primary Color Scheme 294 Use Small Instructions Near the Work to Make Navigation Obvious 295 Filter Information Presented in Crosstabs to Provide Relevant Details-on-Demand 296 Remove All Non-Data-Ink 298 Avoid One-Size-Fits-All Dashboards 298 Work to Achieve Dashboard Load Times of Less Than Ten Seconds 299 Building Your First Advanced Dashboard 299 Introducing the Dashboard Worksheet 299 Position the Worksheet Objects in the Dashboard Workspace 304 Using Layout Containers to Position Objects 308 Positioning the Select Year Text Table and Legends 311 Inserting and Moving Text Objects 312 Positioning and Fitting the Dashboard Objects 315 Ensure That Each Worksheet Object Fits Its Entire View 316 Create More Descriptive Titles for Each Data Pane 317 Improving the Bullet Graph and Sparkline Charts 318 Improving the Text Tables and Scatter Plot 326 Using Actions to Create Advanced Dashboard Navigation 328 Using the Select Year Text Table to Filter the Main Dashboard 329 Adding a Column Heading to Select Year 331 Adding Dynamic Title Content 332 Auto-Generating Highlight Actions from Legends 333 Understanding the Action Dialog Box 336 Embedding a Live Website in a Dashboard 340 Assemble Dashboard 2 345 Adding Details on Demand with Tooltips 354 Enhancing Tooltips and Titles 356 Adding a Read Me Dashboard 358 Bonus: Adding a Floating Dashboard Object 359 Finishing the Titles in the Main Dashboard 363 Sharing Your Dashboard with Tableau Reader 364 Security Considerations for Publishing via Tableau Reader 365 Using the Tableau Performance Recorder to Improve Load Speed 366 Sharing Dashboards with Tableau Online or Tableau Server 367 9 Designing for Mobile 369 The Physics of Mobile Consumption 370 Security Considerations for Mobile Consumption 370 Offline Access 371 Typical Mobile Usage Patterns 373 Just-In-Time Use 373 Mobile Design Implications 374 Design Best Practices for Mobile Consumption 374 Design Implications Related to Screen Resolution 375 Best Practices for Mobile Design 375 Design for a Specific Orientation 375 Consider the Limits of Finger Navigation 375 Reduce the Number of Worksheets Being Displayed 378 A Tablet Dashboard Example 378 Mobile Authoring and Editing 382 A Note on Project Elastic 383 10 Conveying Your Findings with Stories 385 Turning Analysis into Insight 385 Building a Story 386 The Story Workspace 387 A Story Example 389 Formatting Story Points 390 Sharing Your Story Point Deck 391 Part II Server 393 11 Installing Tableau Server 395 What’s New in Version 9? 396 Reasons to Deploy Tableau Server 397 Data Governance 398 Efficiency 398 Flexibility 399 Licensing Options for Tableau Server and Tableau Online 399 Determining Your Hardware and Software Needs 399 New Feature: Persistent Query Cache 401 Determining What Kind of Server License to Purchase 401 Tableau Server’s Architecture 402 Sizing the Server Hardware 403 A Scale-Up Scenario 404 A Scale-Out Scenario 404 Environmental Factors That Can Affect Performance 405 Network Performance 405 Browser 405 Resource Contention 405 Configuring Tableau Server for the First Time 405 General Setup Menu Tab 406 General: Run as User, User Authentication, and Active Directory 407 General: Gateway Port Number 408 General: Open Port in Windows Firewall 408 General: Include Sample Data and Users 408 Data Connection Tab 409 Alerts and Subscriptions 410 Server Processes 411 Security Options 412 External Secure Sockets Layer 414 SAML—Security Assertion Markup Language 415 Kerberos—A Ticket-Based Security Protocol 416 Managing Ownership Through Hierarchy 417 Workbooks and Views 417 User 418 Project 418 Group 418 Site 418 Permissions 419 Permissions for Web Edit, Save, and Download 420 Providing Data Security with User Filters 421 Applying a User Filter to a Data Source 424 Creating a Hybrid Filter from the Data Source 425 What Is the Data Server? 427 When and How to Deploy Server on Multiple Physical Boxes 428 Deploying Tableau Server in High Availability Environments 429 Three-Node Cluster 429 Four-Node Cluster 430 Leveraging Existing Security with Trusted Authentication 432 Deploying Tableau Server in Multi-national Environments 434 Tableau Server Performance Recorder 436 Show Events Filter 438 Timeline Gantt Chart 439 Events Sorted by Time 439 Query Text 439 Performance-Tuning Tactics 439 Query Execution 439 Geocoding 439 Connecting to the Data Source 440 Layout Computations 440 Generating Extract 440 Blending Data 441 Server Rendering 441 Managing Tableau Server in the Cloud 441 What Does It Mean to Be in the Cloud? 441 Tableau’s Cloud-Based Versions of Server 442 Putting Tableau Server in the Cloud 443 Monitoring Activity on Tableau Server 443 Status Section 445 Analysis Section 445 Log Files Section 445 Rebuilt Search Index Section 446 Editing Server Settings and Monitoring Licensing 446 Server Settings General Page 446 Server Setting License Page 447 Partner Add-On Toolkits 448 12 Managing Tableau Server 449 Managing Published Dashboards in Tableau Server 449 Project 451 Name 452 Tags 452 Views to Share 452 Options 452 Edit 453 Navigating Tableau Server 454 Organizing Reports for Consumption 457 Adding Tags to Workbooks 458 Creating a Favorite 459 Options for Securing Reports 461 The Application Layer 461 Defining Custom Roles 462 A Permission-Setting Example 464 Improve Efficiency with the Data Server 469 Publishing a Data Source 469 Consuming Information in Tableau Server 474 Finding Information 475 Authoring and Editing Reports via Server 480 What Is Required to Author Reports on the Web? 480 Server Design and Usage Considerations Related to Web and Tablet Authoring 481 Differences Between Desktop and Web or Tablet Authoring 482 Saving and Exporting via the Web-Tablet Environment 488 Export 488 Save and Save As 489 Recommendations for Implementing Web/Tablet Authoring 489 Sharing Connections, Data Models, and Data Extracts 490 Offering a Common Data Library 490 Sharing Data Models 490 Embedding Tableau Reports Securely on the Web 491 When to Embed a Dashboard 491 When Your Reports Are a Piece of a Larger SaaS Offering 491 Providing a More Robust Environment 492 How to Embed a Dashboard 492 Further Control Using Passed Parameters 494 Tips and Tricks for Embedding Dashboards 494 Using Trusted Ticket Authentication as an Alternative Single Sign-On Method 495 Using Subscriptions to Deliver Reports via E‑mail 496 Creating Subscription Schedules 496 13 Automating Tableau Server 501 Tableau Server’s APIs 501 What Do Tabcmd and Tabadmin Do? 502 Installing the Command-Line Tools 502 Setting the Windows Path 505 What Kind of Tasks Can Tabcmd Do? 506 Learning to Leverage Tabcmd 507 Manually Entering and Running a Script in Tabcmd 508 Running Tabcmd Scripts via Batch Files 509 The Steps Required to Create Batch Processing Scripts 509 Using Windows Scheduler to Fully Automate Scripts 511 Common Use Cases for Tabcmd 513 Automating Extracts with the Extract API 515 Data Extract API 515 Using the Extract API with Python 517 Data Extract Command-Line Utility 520 REST API 521 Initial Transactions 521 Part III Case Studies 527 14 Ensuring a Successful Tableau Deployment 529 Deploying Tableau—Lessons Learned 529 Effective Use of Consultants 529 Your Team’s Current Knowledge 530 The Data Landscape 530 The Tableau User Group at Cigna 531 Taking Care of Vizness 531 Resourcing 532 Cadence 532 Format 533 Topics 533 Effectiveness and Attendance 534 Tracking Participation 535 Success 535 Part IV Appendixes 537 A Tableau’s Product Ecosystem 539 B S upported Data Source Connections 543 C Keyboard Shortcuts 547 D Recommended Hardware Configurations 551 E Understanding Tableau Functions 555 F Companion Website 657 Glossary 659 Index 673

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Book SynopsisThis new edition to the classic book by ggplot2 creator Hadley Wickham highlights compatibility with knitr and RStudio. ggplot2 is a data visualization package for R that helps users create data graphics, including those that are multi-layered, with ease. With ggplot2, it's easy to: produce handsome, publication-quality plots with automatic legends created from the plot specification superimpose multiple layers (points, lines, maps, tiles, box plots) from different data sources with automatically adjusted common scales add customizable smoothers that use powerful modeling capabilities of R, such as loess, linear models, generalized additive models, and robust regression save any ggplot2 plot (or part thereof) for later modification or reuse create custom themes that capture in-house or journal style requirements and that can easily be applied to multiple plots approach a graph from a visual perspective, thinking about how each component of the data is represented on the final plot This book will be useful to everyone who has struggled with displaying data in an informative and attractive way. Some basic knowledge of R is necessary (e.g., importing data into R). ggplot2 is a mini-language specifically tailored for producing graphics, and you'll learn everything you need in the book. After reading this book you'll be able to produce graphics customized precisely for your problems, and you'll find it easy to get graphics out of your head and on to the screen or page.Trade Review“The versatility and efficiency of ggplot have led to the development of ggplot2 and this book which overviews the standard use and presentation secrets of functions developed in the last 5 years. … The book is written in an accessible manner and it is suitable for undergraduates, postgraduates and researchers with some R experience. All theoretical concepts are accompanied by code making it easy to learn by reproducing the examples.” (Irina Ioana Mohorianu, zbMATH 1397.62006, 2018)“The book is an excellent and very comprehensive manual of … one of the most popular R packages. It is currently the only book describing ggplot2 in such depth. The book contains many examples and is very nicely illustrated, demonstrating the strength of the package.” (Klaus Galensa, Computing Reviews, May, 2017)Table of ContentsIntroduction.- Getting Started with ggplot2.- Toolbox.- Mastering the Grammar.- Building a Plot Layer by Layer.- Scales, Axes and Legends.- Positioning.- Themes.- Data Analysis.- Data Transformation.- Modelling for Visualisation.- Programming with ggplot2.- Index.- R Code Index.

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Book SynopsisThis textbook presents an introduction to multiple linear regression, providing real-world data sets and practice problems. A practical working knowledge of applied statistical practice is developed through the use of these data sets and numerous case studies. The authors include a set of practice problems both at the end of each chapter and at the end of the book. Each example in the text is cross-referenced with the relevant data set, so that readers can load the data and follow the analysis in their own R sessions. The balance between theory and practice is evident in the list of problems, which vary in difficulty and purpose.This book is designed with teaching and learning in mind, featuring chapter introductions and summaries, exercises, short answers, and simple, clear examples. Focusing on the connections between generalized linear models (GLMs) and linear regression, the book also references advanced topics and tools that have not typically been included inTrade Review“This is a great book … . The book comprehensively covers almost everything you need to know or teach in this area. This book is an invaluable reference either as a classroom text or for the researcher’s bookshelf.” (Pablo Emilio Verde, ISCB News, iscb.info, Issue 69, July, 2020)“I congratulate the authors for making an important contribution in this field. … the book represents an excellent and very comprehensible introduction into the world of generalized linear models and is recommended for all readers who are looking for a practical introduction to this topic using R.” (Dominic Edelmann, Biometrical Journal, Vol. 62, 2020)“The book is targeted at students and notes it is appropriate for graduate students. It is also useful to the junior statistician needing to learn how to work a model they are unfamiliar with. The practicing and experienced statistician can use this as a quick reference for working a model they may have forgotten the specific of.” (James P. Howard II, zbMath 1416.62020, 2019)Table of ContentsStatistical models.- Linear regression models.- Linear regression models: diagnostics and model-building.- Beyond linear regression: the method of maximum likelihood.- Generalized linear models: structure.- Generalized linear models: estimation.- Generalized linear models: inference.- Generalized linear models: diagnostics.- Models for proportions: binomial GLMs.- Models for counts: Poisson and negative binomial GLMs.- Positive continuous data: gamma and inverse Gaussian GLMs.- Tweedie GLMs.- Extra problems.- Appendix A: Using R for data analysis.- Appendix B: The GLMsData package.- Index: Data sets.- Index: R commands.- Index: General Topics.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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Book SynopsisSAS programming is a creative and iterative process designed to empower you to make the most of your organization''s data. This friendly guide provides you with a repertoire of essential SAS tools for data management, whether you are a new or an infrequent user. Most useful to students and programmers with little or no SAS experience, it takes a no-frills, hands-on tutorial approach to getting started with the software. You will find immediate guidance in navigating, exploring, visualizing, cleaning, formatting, and reporting on data using SAS and JMP. Step-by-step demonstrations, screenshots, handy tips, and practical exercises with solutions equip you to explore, interpret, process and summarize data independently, efficiently and effectively.Trade Review'The authors have created a very readable and gentle introduction to SAS programming and its working environment - Enterprise Guide. The text provides a valuable overview of 'navigating' in a SAS windowing environment and before moving quickly into core procedures. … a very valuable introduction to basic SAS programming for the beginning data analyst.' Glenn Gamst, University of la Verne, California'The authors of Data Management Essentials Using SAS and JMP have written a thoroughly user-friendly beginning guide to SAS programming for data exploration and management, and for designing well-constructed and informative reports. I honestly know of no better book to use for self-instruction on, or for teaching essential SAS programming skills for report design than this very well written and produced book.' Joseph M. Hilbe, Arizona State UniversityTable of Contents1. Navigation; 2. Preliminary data exploration; 3. Storing and manipulating data; 4. Advanced concepts in dataset and variable manipulation; 5. Introduction to common procedures; 6. Procedures for simple statistics; 7. More about common procedures; 8. Data visualization; 9. JMP as an alternative.

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Book SynopsisThis innovative approach to teaching the finite element method blends theoretical, textbook-based learning with practical application using online and video resources. This hybrid teaching package features computational software such as MATLAB, and tutorials presenting software applications such as PTC Creo Parametric, ANSYS APDL, ANSYS Workbench and SolidWorks, complete with detailed annotations and instructions so students can confidently develop hands-on experience. Suitable for senior undergraduate and graduate level classes, students will transition seamlessly between mathematical models and practical commercial software problems, empowering them to advance from basic differential equations to industry-standard modelling and analysis. Complete with over 120 end-of chapter problems and over 200 illustrations, this accessible reference will equip students with the tools they need to succeed in the workplace.Trade Review'This book provides an excellent balance between the theoretical side, which is critical for students to understand essentials in the implementation procedure of the finite element method, and the application side in using commonly available finite element packages such as ANSYS. The examples illustrated in this book are particularly interesting, thorough, and easy to follow.' Qin Ma, Walla Walla University'The perfect material for people who want to learn about or teach the theoretical background of the FEM. It explains key concepts with plenty of examples, MATLAB® codes and practice problems. The book also provides theories of element locking and mixed formulation, and detailed formulations for structural dynamics and heat transfer, showing how the FEM can be applied to various engineering problems.' Hoon Cheol Park, Konkuk University'I strongly recommend this book to students who want to learn about the fundamentals of the finite element method. Professors Lee and Chung are well known for their contribution to the FEM field. The book focuses on the FEM using linear structural analysis, and Chapter 11 applies FEM to heat transfer problems, an excellent addition which opens the concept of FEM to solve partial differential equations.' Sameer Mulani, University of AlabamaTable of ContentsPreface; 1. Introduction to the finite element method; 2. Truss, temperature effect and torsion; 3. Beams and frames; 4. Structural dynamics; 5. Bending under axial force; 6. Virtual displacement and virtual work; 7. Mapping, shape functions and numerical integration; 8. Two and three dimensional deformable solid bodies; 9. Plates and shells; 10. Element locking; 11. Heat transfer; Appendix 1. Fundamentals of solid and structural mechanics; Appendix 2. Solution methods; Bibliography; Index.

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Book Synopsis

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Book Synopsis

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Book SynopsisR is a statistical computer program used and developed by statisticians around the world. It is probably the leading statistical program, at least among statisticians, and it is freely available. This book is intended for the newcomer who wants to do statistical analysis with R and needs a guide to get started. The book focuses on statistical data problems that are often encountered within the biosceinces. It puts special emphasis on linear models and analysis of repeated measurements data, but also deals with binary data and survival data, among others. Problems are presented and solutions -- along with the corresponding OR code and output -- are provided. The guide is divided into two parts: the first part on R basics and the second part on the statistical analyses using R. Various datasets are used for illustration and they are all available in the R package Guide1data.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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a huge range and FREE tracked UK delivery on ALL orders.

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Book SynopsisWe have sold 4300 copies worldwide of the first edition (1999). This new edition contains five completely new chapters covering new developments. Trade ReviewFrom the reviews: MATHEMATICAL REVIEWS "Although the book is written as a textbook, with many carefully worked out examples and exercises, it will be very useful for the researcher since the authors discuss their favorite research topics (Monte Carlo optimization and convergence diagnostics) going through many relevant references…This book is a comprehensive treatment of the subject and will be an essential reference for statisticians working with McMC." From the reviews of the second edition: "Only 2 years after its first edition this carefully revised second edition accounts for the rapid development in this field...This book can be highly recommended for students and researchers interested in learning more about MCMC methods and their background." Biometrics, March 2005 "This is a comprehensive book for advanced graduate study by statisticians." Technometrics, May 2005 "This excellent text is highly recommended..." Short Book Reviews of the ISI, April 2005 "This book provides a thorough introduction to Monte Carlo methods in statistics with an emphasis on Markov chain Monte Carlo methods. … Each chapter is concluded by problems and notes. … The book is self-contained and does not assume prior knowledge of simulation or Markov chains. …. on the whole it is a readable book with lots of useful information." (Søren Feodor Nielsen, Journal of Applied Statistics, Vol. 32 (6), August, 2005) "This revision of the influential 1999 text … includes changes to the presentation in the early chapters and much new material related to MCMC and Gibbs sampling. The result is a useful introduction to Monte Carlo methods and a convenient reference for much of current methodology. … The numerous problems include many with analytical components. The result is a very useful resource for anyone wanting to understand Monte Carlo procedures. This excellent text is highly recommended … ." (D.F. Andrews, Short Book Reviews, Vol. 25 (1), 2005) "You have to practice statistics on a desert island not to know that Markov chain Monte Carlo (MCMC) methods are hot. That situation has caused the authors not only to produce a new edition of their landmark book but also to completely revise and considerably expand it. … This is a comprehensive book for advanced graduate study by statisticians." (Technometrics, Vol. 47 (2), May, 2005) "This remarkable book presents a broad and deep coverage of the subject. … This second edition is a considerably enlarged version of the first. Some subjects that have matured more rapidly in the five years following the first edition, like reversible jump processes, sequential MC, two-stage Gibbs sampling and perfect sampling have now chapters of their own. … the book is also very well suited for self-study and is also a valuable reference for any statistician who wants to study and apply these techniques." (Ricardo Maronna, Statistical Papers, Vol. 48, 2006) "This second edition of ‘Monte Carlo Statistical Methods’ has appeared only five years after the first … the new edition aims to incorporate recent developments. … Each chapter includes sections with problems and notes. … The style of the presentation and many carefully designed examples make the book very readable and easily accessible. It represents a comprehensive account of the topic containing valuable material for lecture courses as well as for research in this area." (Evelyn Buckwar, Zentrablatt MATH, Vol. 1096 (22), 2006) "This is a useful and utilitarian book. It provides a catalogue of modern Monte carlo based computational techniques with ultimate emphasis on Markov chain Monte Carlo (MCMC) … . an excellent reference for anyone who is interested in algorithms for various modes of Markov chain (MC) methodology … . a must for any researcher who believes in the importance of understanding what goes on inside of the MCMC ‘black box.’ … I recommend the book to all who wish to learn about statistical simulation." (Wesley O. Johnson, Journal of the American Statistical Association, Vol. 104 (485), March, 2009)Table of ContentsIntroduction * Random Variable Generation * Monte Carlo Integration * Controlling Monte Carlo Variance * Monte Carlo Optimization * Markov Chains * The Metropolis-Hastings Algorithm * The Slice Sampler * The Two-Stage Gibbs Sampler * The Multi-Stage Gibbs Sampler * Variable Dimension Models and Reversible Jump * Diagnosing Convergence * Perfect Sampling * Iterated and Sequential Importance Sampling

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Trade ReviewFrom the reviews of the second edition: "This fascinating book deconstructs the process of producing graphics and in doing so raises many fascinating questions on the nature and representation of information...This second edition is almost twice the size of the original, with six new chapters and substantial revisions." Short Book Reviews of the International Statistical Institute, December 2005 "When the first edidtion of this book appeared in 2000 it was much praised. I called it a tour de force of the highest order. (Wainer, 2001), Edward Wegman (2000) argued that it was destined to become a classic. Now, six years later this very fine book has been much improved." Howard Wainer for Psychometrika "...The second edition is an impressive expansion beyond a quite remarkable first edition. The text remains dense and even more encyclopedic, but it is a pleasure to read, whether a novice or an expert in graphics...this book is a bargain...The second edition is a must-have volume for anyone interested in graphics." Thomas E. Bradstreet for the Journal of the American Statistical Association, December 2006 "I find myself still thinking about the book and its ideas, several weeks after I finished reading it. I love that kind of book." Mark Bailey for Techometrics, Vol. 49, No. 1, February 2007 "Warts and all, The Grammar of Graphics is a richly rewarding work, an outstanding achievement by one of the leaders of statistical graphics. Seek it out." Nicholas J. Cox for the Journal of Statistical Software, January 2007 "The second edition is a quite fascinating book as well, and it comes with many color graphics. Anyone working in this field can see how many hours the author (plus coworkers) has spent on such a volume. … Demands for good graphics are high and this book will help to wetten the appetite to create future computer packages that will meet this demand. An occasional reader will get insights into a modern world of computing … ." (Wolfgang Polasek, Statistical Papers, Vol. 48, 2007)Table of ContentsSyntax.- How To Make a Pie.- Data.- Variables.- Algebra.- Scales.- Statistics.- Geometry.- Coordinates.- Aesthetics.- Facets.- Guides.- Semantics.- Space.- Time.- Uncertainty.- Analysis.- Control.- Automation.- Reader.- Coda.

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Book Synopsisand the building of the Three “M’s” Maple, Mathematica and Matlab. We intend to persuade that Maple and other like tools are worth knowing assuming only that one wishes to be a mathematician, a mathematics educator, a computer scientist, an engineer or scientist, or anyone else who wishes/needs to use mathematics better.Trade ReviewFrom the reviews:“This book is intended to teach the reader the usage of the computer algebra system Maple. … The book is readable and valuable to mathematics, science, and engineering undergraduates at the sophomore or above level. It could also be valuable to practitioners in those fields who want to learn Maple in situ. … Summing Up: Recommended. Lower-division undergraduates through graduate students; professionals.” (D. Z. Spicer, Choice, Vol. 49 (5), January, 2012)“This is a Maple-application book which illustrates some basic areas of mathematics by symbolic computation examples. … The presentation is clear with all necessary details and comments for ensuring a full understanding of the considered examples. The intended beneficiaries are undergraduate students, teachers giving courses to undergraduate students, as well as programmers interested in using Maple for several classes of mathematical problems.” (Octavian Pastravanu, Zentralblatt MATH, Vol. 1228, 2012)“In An Introduction to Modern Mathematical Computing with Maple, Borwein and Skerritt show that computers are an excellent companion for learning mathematics. … The theme of the book is that Maple can supplement mathematics learning and, what is more, can do much of the mathematics for the students. … The temptation is tremendous for students to skip the real work to have a true understanding of mathematics.” (David S. Mazel, The Mathematical Association of America, June, 2012)Table of Contents-Preface. -Conventions and Notation.-1. Number Theory (Introduction to Maple, Putting it together, Enough code, already. Show me some maths!, Problems and Exercises, Further Explorations). -2. Calculus(Revision and Introduction, Univariate Calculus, Multivariate Calculus, Exercises, Further Explorations). -3. Linear Algebra (Introduction and Review, Vector Spaces, Linear Transformations, Exercises, Further Explorations). -4. Visualisation and Geometry: a postscript (Useful Visualisation Tools, Geometry and Geometric Constructions). –A. Sample Quizzes (Number Theory, Calculus, Linear Algebra). –Index. –References

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Book SynopsisThe techniques and methods presented for knowledge elicitation, model construction and verification, modeling techniques and tricks, learning models from data, and analyses of models have all been developed and refined on the basis of numerous courses that the authors have held for practitioners worldwide.Trade ReviewFrom the book reviews:“The monograph concentrates on intelligent systems for decision support based on probabilistic models, including Bayesian networks and influence diagrams. … This monograph provides a review of recent state affairs of probabilistic networks that can be useful for professionals, practitioners, and researchers from diverse fields of statistics and related disciplines. I think it can be used as a textbook in its own right for an upper level undergraduate course, especially for a reading course.” (Technometrics, Vol. 55 (2), May, 2013)Table of ContentsIntroduction.- Networks.- Probabilities.- Probabilistic Networks.- Solving Probabilistic Networks.- Eliciting the Model.- Modeling Techniques.- Data-Driven Modeling.- Conflict Analysis.- Sensitivity Analysis.- Value of Information Analysis.- Quick Reference to Model Construction.- List of Examples.- List of Figures.- List of Tables.- List of Symbols.- References.- Index.

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Book SynopsisThe techniques and methods presented for knowledge elicitation, model construction and verification, modeling techniques and tricks, learning models from data, and analyses of models have all been developed and refined on the basis of numerous courses that the authors have held for practitioners worldwide.Trade ReviewFrom the book reviews:“The monograph concentrates on intelligent systems for decision support based on probabilistic models, including Bayesian networks and influence diagrams. … This monograph provides a review of recent state affairs of probabilistic networks that can be useful for professionals, practitioners, and researchers from diverse fields of statistics and related disciplines. I think it can be used as a textbook in its own right for an upper level undergraduate course, especially for a reading course.” (Technometrics, Vol. 55 (2), May, 2013)Table of ContentsIntroduction.- Networks.- Probabilities.- Probabilistic Networks.- Solving Probabilistic Networks.- Eliciting the Model.- Modeling Techniques.- Data-Driven Modeling.- Conflict Analysis.- Sensitivity Analysis.- Value of Information Analysis.- Quick Reference to Model Construction.- List of Examples.- List of Figures.- List of Tables.- List of Symbols.- References.- Index.

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