A new paradigm for scientific discovery through computational tools now permeates every aspect of astronomical research. Computational astrophysics combines modern computational methods, novel hardware designs, advanced algorithms, original software implementations, and associated technologies to discover new phenomena and to make predictions in astronomy. The proceedings of IAU Symposium 362 summarizes ongoing developments in computational astrophysics through astronomers in diverse fields sharing their knowledge and approaches. It focuses on computational methods applied to speed up and broaden the scope of scientific studies, such as finding trends in observational data, high performance computing, automated search algorithms, and model predictability. Experts discuss a palette of challenging informational and technical developments, with the goal of coordinating their efforts and the improvement of techniques in pursuit of a wide range of astronomical studies, including fluid dynamics in star and galaxy evolution, exoplanets, gravitational waves, numerical relativity, data mining, and much more.

Das St. Galler Modell fur prozesszentriertes Customer Relationship Management basiert auf Praxiserfahrungen, die in acht Fallstudien fuhrender Unternehmen dokumentiert sind: Ganzheitliches Kundenbindungsmarketing der Direkt Anlage Bank; Contact Center der Swisscom; Kampagnen- und Kundenmanagement bei Genossenschaftsbanken; Kundenzentrierte Prozesse und Systeme der Credit Suisse, LGT Bank in Liechtenstein und Neuen Zurcher Zeitung; Management von Projekt- und Kundenwissen bei der SAP. Das Gesamtmodell beschreibt mit Kunden-, Kanal- sowie Prozess- und Wissensmanagement die wesentlichen Instrumente zur radikalen Ausrichtung auf Kundenprozesse. Eine Ubersicht der achtzehn wichtigsten Einfuhrungsmethoden aus Literatur, Beratung und von Systemanbietern unterstutzt die erfolgreiche Projektdurchfuhrung."

An easy-to-understand course book, based on the authentic lectures and detailed research, conducted by the authors themselves, on information optics, holography and MATLAB. This book is the first to highlight the incoherent optical system, provide up-to-date, novel digital holography techniques, and demonstrate MATLAB codes to accomplish tasks such as optical image processing and pattern recognition. This title is a comprehensive introduction to the basics of Fourier optics as well as optical image processing and digital holography. A step-by-step guide which details the vast majority of the derivations, without omitting essential steps, to facilitate a clear mathematical understanding. This book also features exercises at the end of each chapter, providing hands-on experience and consolidating understanding. An ideal companion for graduates and researchers involved in engineering and applied physics, as well as interested in the growing field of information optics.

This book covers applications of R to the general discipline of radiation dosimetry and to the specific areas of luminescence dosimetry, luminescence dating, and radiation protection dosimetry. It features more than 90 detailed worked examples of R code fully integrated into the text, with extensive annotations. The book shows how researchers can use available R packages to analyze their experimental data, and how to extract the various parameters describing mathematically the luminescence signals. In each chapter, the theory behind the subject is summarized, and references are given from the literature, so that researchers can look up the details of the theory and the relevant experiments. Several chapters are dedicated to Monte Carlo methods, which are used to simulate the luminescence processes during the irradiation, heating, and optical stimulation of solids, for a wide variety of materials. This book will be useful to those who use the tools of luminescence dosimetry, including physicists, geologists, archaeologists, and for all researchers who use radiation in their research.

This book shows you how to analyze data sets systematically and to use Excel 2019 to extract information from data almost effortlessly. Both are (not) an art! The statistical methods are presented and discussed using a single data set. This makes it clear how the methods build on each other and gradually more and more information can be extracted from the data. The Excel functions used are explained in detail - the procedure can be easily transferred to other data sets. Various didactic elements facilitate orientation and working with the book: At the checkpoints, the most important aspects from each chapter are briefly summarized. In the freak knowledge section, more advanced aspects are addressed to whet the appetite for more. All examples are calculated with hand and Excel. Numerous applications and solutions as well as further data sets are available on the author's internet platform. This book is a translation of the original German 2nd edition Statistik angewandt mit Excel by Franz Kronthaler, published by Springer-Verlag GmbH Germany, part of Springer Nature in 2021. The translation was done with the help of artificial intelligence (machine translation by the service DeepL.com). A subsequent human revision was done primarily in terms of content, so that the book will read stylistically differently from a conventional translation. Springer Nature works continuously to further the development of tools for the production of books and on the related technologies to support the authors.

BRIDGES THE GAP BETWEEN SAS AND R, ALLOWING USERS TRAINED IN ONE LANGUAGE TO EASILY LEARN THE OTHER SAS and R are widely-used, very different software environments. Prized for its statistical and graphical tools, R is an open-source programming language that is popular with statisticians and data miners who develop statistical software and analyze data. SAS (Statistical Analysis System) is the leading corporate software in analytics thanks to its faster data handling and smaller learning curve. SAS for R Users enables entry-level data scientists to take advantage of the best aspects of both tools by providing a cross-functional framework for users who already know R but may need to work with SAS. Those with knowledge of both R and SAS are of far greater value to employers, particularly in corporate settings. Using a clear, step-by-step approach, this book presents an analytics workflow that mirrors that of the everyday data scientist. This up-to-date guide is compatible with the latest R packages as well as SAS University Edition. Useful for anyone seeking employment in data science, this book: Instructs both practitioners and students fluent in one language seeking to learn the other Provides command-by-command translations of R to SAS and SAS to R Offers examples and applications in both R and SAS Presents step-by-step guidance on workflows, color illustrations, sample code, chapter quizzes, and more Includes sections on advanced methods and applications Designed for professionals, researchers, and students, SAS for R Users is a valuable resource for those with some knowledge of coding and basic statistics who wish to enter the realm of data science and business analytics.

Learn to code like a professional with Python - an open source, versatile, and powerful programming language About This Book * Learn the fundamentals of programming with Python - one of the best languages ever created * Develop a strong set of programming skills that you will be able to express in any situation, on every platform, thanks to Python's portability * Create outstanding applications of all kind, from websites to scripting, and from GUIs to data science Who This Book Is For Python is the most popular introductory teaching language in U.S. top computer science universities, so if you are new to software development, or maybe you have little experience, and would like to start off on the right foot, then this language and this book are what you need. Its amazing design and portability will help you become productive regardless of the environment you choose to work with. What You Will Learn * Get Python up and running on Windows, Mac, and Linux in no time * Grasp the fundamental concepts of coding, along with the basics of data structures and control flow. * Write elegant, reusable, and efficient code in any situation * Understand when to use the functional or the object oriented programming approach * Create bulletproof, reliable software by writing tests to support your code * Explore examples of GUIs, scripting, data science and web applications * Learn to be independent, capable of fetching any resource you need, as well as dig deeper In Detail Learning Python has a dynamic and varied nature. It reads easily and lays a good foundation for those who are interested in digging deeper. It has a practical and example-oriented approach through which both the introductory and the advanced topics are explained. Starting with the fundamentals of programming and Python, it ends by exploring very different topics, like GUIs, web apps and data science. The book takes you all the way to creating a fully fledged application. The book begins by exploring the essentials of programming, data structures and teaches you how to manipulate them. It then moves on to controlling the flow of a program and writing reusable and error proof code. You will then explore different programming paradigms that will allow you to find the best approach to any situation, and also learn how to perform performance optimization as well as effective debugging. Throughout, the book steers you through the various types of applications, and it concludes with a complete mini website built upon all the concepts that you learned. Style and approach This book is an easy-to-follow guide that will take you from a novice to the proficient level at a comfortable pace, using a lot of simple but effective examples. Each topic is explained thoroughly, and pointers are left for the more inquisitive readers to dig deeper and expand their knowledge.

This book is open access under a CC BY 4.0 license This open access book brings together the latest genome base prediction models currently being used by statisticians, breeders and data scientists. It provides an accessible way to understand the theory behind each statistical learning tool, the required pre-processing, the basics of model building, how to train statistical learning methods, the basic R scripts needed to implement each statistical learning tool, and the output of each tool. To do so, for each tool the book provides background theory, some elements of the R statistical software for its implementation, the conceptual underpinnings, and at least two illustrative examples with data from real-world genomic selection experiments. Lastly, worked-out examples help readers check their own comprehension.The book will greatly appeal to readers in plant (and animal) breeding, geneticists and statisticians, as it provides in a very accessible way the necessary theory, the appropriate R code, and illustrative examples for a complete understanding of each statistical learning tool. In addition, it weighs the advantages and disadvantages of each tool.

Refer to the practical guidance provided in this book to develop Salesforce custom applications in a more agile, collaborative, and resilient way using Salesforce Developer Experience (DX). You will learn how to use the Salesforce Command Line Interface (CLI) to simplify working with projects, metadata, data and orgs. The CLI integrates with your development tools of choice such as Visual Studio Code, and CI/CD tools to implement DevOps pipelines. Readers will also gain an understanding of the package development model, which improves application quality and maintainability by grouping metadata into highly cohesive, loosely coupled containers. Salesforce DX supports application development throughout the entire development lifecycle where a version control system, rather than a Salesforce org, is the source of truth. It became generally available in late 2017 and has now reached a stage of feature richness and stability that it is becoming more widely adopted. Beginning Salesforce DX provides development teams with practical, how-to examples of using Salesforce DX that go beyond the Salesforce documentation. Commands and their parameters are described, including any gotchas, and the outcome of the commands on a Salesforce org is explained. What You Will Learn * How to setup a Salesforce DX development environment * Understand the key Salesforce DX concepts and the Salesforce CLI * Work with Dev Hubs, projects, orgs, metadata and version control systems * Improve quality with test users and test data * Bootstrap pro-code development with templates * Apply Salesforce DX to an end-to-end package development project Who This Book Is For Internal teams developing custom Salesforce applications for an individual customer, or those creating commercial applications for distribution via the Salesforce AppExchange enterprise marketplace. All team disciplines will benefit from understanding and applying Salesforce DX, including pro-code, low-code and no-code developers, testers, release managers, DevOps engineers and administrators. A secondary audience includes those needing to understand key concepts when establishing or evolving an organisation's application lifecycle management capability, such as capability leaders, architects, consultants and business analysts.

Master the syntax for working with R's plotting functions in graphics and stats in this easy reference to formatting plots. The approach in Visualizing Data in R 4 toward the application of formatting in ggplot() will follow the structure of the formatting used by the plotting functions in graphics and stats. This book will take advantage of the new features added to R 4 where appropriate including a refreshed color palette for charts, Cairo graphics with more fonts/symbols, and improved performance from grid graphics including ggplot 2 rendering speed. Visualizing Data in R 4 starts with an introduction and then is split into two parts and six appendices. Part I covers the function plot() and the ancillary functions you can use with plot(). You'll also see the functions par() and layout(), providing for multiple plots on a page. Part II goes over the basics of using the functions qplot() and ggplot() in the package ggplot2. The default plots generated by the functions qplot() and ggplot() give more sophisticated-looking plots than the default plots done by plot() and are easier to use, but the function plot() is more flexible. Both plot() and ggplot() allow for many layers to a plot. The six appendices will cover plots for contingency tables, plots for continuous variables, plots for data with a limited number of values, functions that generate multiple plots, plots for time series analysis, and some miscellaneous plots. Some of the functions that will be in the appendices include functions that generate histograms, bar charts, pie charts, box plots, and heatmaps. What You Will Learn Use R to create informative graphics Master plot(), qplot(), and ggplot() Discover the canned graphics functions in stats and graphics Format plots generated by plot() and ggplot() Who This Book Is For Those in data science who use R. Some prior experience with R or data science is recommended.

Employ essential tools and functions of the MATLAB and Simulink packages, which are explained and demonstrated via interactive examples and case studies. This revised edition covers features from the latest MATLAB 2022b release, as well as other features that have been released since the first edition published. This book contains dozens of simulation models and solved problems via m-files/scripts and Simulink models which will help you to learn programming and modelling essentials. You'll become efficient with many of the built-in tools and functions of MATLAB/Simulink while solving engineering and scientific computing problems. Beginning MATLAB and Simulink, Second Edition explains various practical issues of programming and modelling in parallel by comparing MATLAB and Simulink. After studying and using this book, you'll be proficient at using MATLAB and Simulink and applying the source code and models from the book's examples as templates for your own projects in data science or engineering. What You Will Learn Master the programming and modelling essentials of MATLAB and Simulink Carry out data visualization with MATLAB Build a GUI and develop App with MATLAB Work with integration and numerical root finding methods Apply MATLAB to differential equations-based models and simulations Use MATLAB and Simulink for data science projects Who This Book Is ForEngineers, programmers, data scientists, and students majoring in engineering and scientific computing who are new to MATLAB and Simulink.

The main focus of this book is on presenting advances in fuzzy statistics, and on proposing a methodology for testing hypotheses in the fuzzy environment based on the estimation of fuzzy confidence intervals, a context in which not only the data but also the hypotheses are considered to be fuzzy. The proposed method for estimating these intervals is based on the likelihood method and employs the bootstrap technique. A new metric generalizing the signed distance measure is also developed. In turn, the book presents two conceptually diverse applications in which defended intervals play a role: one is a novel methodology for evaluating linguistic questionnaires developed at the global and individual levels; the other is an extension of the multi-ways analysis of variance to the space of fuzzy sets. To illustrate these approaches, the book presents several empirical and simulation-based studies with synthetic and real data sets. In closing, it presents a coherent R package called "FuzzySTs" which covers all the previously mentioned concepts with full documentation and selected use cases. Given its scope, the book will be of interest to all researchers whose work involves advanced fuzzy statistical methods.

Watch Andy talk about the new version of his book for R: click here Hot on the heels of the award-winning and best selling Discovering Statistics Using SPSS Third Edition, Andy Field has teamed up with Jeremy Miles (co-author of Discovering Statistics Using SAS) to write Discovering Statistics Using R. Keeping the uniquely humorous and self-depreciating style that has made students across the world fall in love with Andy Field's books, Discovering Statistics Using R takes students on a journey of statistical discovery using the freeware R, a free, flexible and dynamically changing software tool for data analysis that is becoming increasingly popular across the social and behavioral sciences throughout the world. The journey begins by explaining basic statistical and research concepts before a guided tour of the R software environment. Next the importance of exploring and graphing data will be discovered, before moving onto statistical tests that are the foundations of the rest of the book (for e.g. correlation and regression). Readers will then stride confidently into intermediate level analyses such as ANOVA, before ending their journey with advanced techniques such as MANOVA and multilevel models. Although there is enough theory to help the reader gain the necessary conceptual understanding of what they're doing, the emphasis is on applying what's learned to playful and real-world examples that should make the experience more fun than expected. Like its sister textbooks, Discovering Statistics Using R is written in an irreverent style and follows the same ground-breaking structure and pedagogical approach. The core material is augmented by a cast of characters to help the reader on their way, hundreds of examples, self-assessment tests to consolidate knowledge, and additional website material for those wanting to learn more (at www.sagepub.co.uk/fieldandmilesR). Given this book's accessibility, fun spirit, and use of bizarre real-w

This book is a result of a workshop, the 8th of the successful TopoInVis workshop series, held in 2019 in Nykoeping, Sweden. The workshop regularly gathers some of the world's leading experts in this field. Thereby, it provides a forum for discussions on the latest advances in the field with a focus on finding practical solutions to open problems in topological data analysis for visualization. The contributions provide introductory and novel research articles including new concepts for the analysis of multivariate and time-dependent data, robust computational approaches for the extraction and approximations of topological structures with theoretical guarantees, and applications of topological scalar and vector field analysis for visualization. The applications span a wide range of scientific areas comprising climate science, material sciences, fluid dynamics, and astronomy. In addition, community efforts with respect to joint software development are reported and discussed.

This book provides a comprehensive and concrete illustration of time series analysis focusing on the state-space model, which has recently attracted increasing attention in a broad range of fields. The major feature of the book lies in its consistent Bayesian treatment regarding whole combinations of batch and sequential solutions for linear Gaussian and general state-space models: MCMC and Kalman/particle filter. The reader is given insight on flexible modeling in modern time series analysis. The main topics of the book deal with the state-space model, covering extensively, from introductory and exploratory methods to the latest advanced topics such as real-time structural change detection. Additionally, a practical exercise using R/Stan based on real data promotes understanding and enhances the reader's analytical capability.

The nonequilibrium behavior of nanoscopic and biological systems, which are typically strongly fluctuating, is a major focus of current research. Lately, much progress has been made in understanding such systems from a thermodynamic perspective. However, new theoretical challenges emerge when the fluctuating system is additionally subject to time delay, e.g. due to the presence of feedback loops. This thesis advances this young and vibrant research field in several directions. The first main contribution concerns the probabilistic description of time-delayed systems; e.g. by introducing a versatile approximation scheme for nonlinear delay systems. Second, it reveals that delay can induce intriguing thermodynamic properties such as anomalous (reversed) heat flow. More generally, the thesis shows how to treat the thermodynamics of non-Markovian systems by introducing auxiliary variables. It turns out that delayed feedback is inextricably linked to nonreciprocal coupling, information flow, and to net energy input on the fluctuating level.

Ideal for researchers and graduate students in the social sciences who require knowledge of structural equation modeling techniques to answer substantive research questions, Using Mplus for Structural Equation Modeling provides a reader-friendly introduction to the major types of structural equation models implemented in the Mplus framework. This practical book, which updates author E. Kevin Kelloway's 1998 book Using LISREL for Structural Equation Modeling, retains the successful five-step process employed in the earlier book, with a thorough update for use in the Mplus environment. Kelloway provides an overview of structural equation modeling techniques in Mplus, including the estimation of confirmatory factor analysis and observed variable path analysis. He also covers multilevel modeling for hypothesis testing in real life settings and offers an introduction to the extended capabilities of Mplus, such as exploratory structural equation modeling and estimation and testing of mediated relationships. A sample application with the source code, printout, and results is presented for each type of analysis.

This book offers a systematic and rigorous treatment of continuous-time Markov decision processes, covering both theory and possible applications to queueing systems, epidemiology, finance, and other fields. Unlike most books on the subject, much attention is paid to problems with functional constraints and the realizability of strategies. Three major methods of investigations are presented, based on dynamic programming, linear programming, and reduction to discrete-time problems. Although the main focus is on models with total (discounted or undiscounted) cost criteria, models with average cost criteria and with impulsive controls are also discussed in depth. The book is self-contained. A separate chapter is devoted to Markov pure jump processes and the appendices collect the requisite background on real analysis and applied probability. All the statements in the main text are proved in detail. Researchers and graduate students in applied probability, operational research, statistics and engineering will find this monograph interesting, useful and valuable.

Text Mining with MATLAB (R) provides a comprehensive introduction to text mining using MATLAB. It is designed to help text mining practitioners, as well as those with little-to-no experience with text mining in general, familiarize themselves with MATLAB and its complex applications. The book is structured in three main parts: The first part, Fundamentals, introduces basic procedures and methods for manipulating and operating with text within the MATLAB programming environment. The second part of the book, Mathematical Models, is devoted to motivating, introducing, and explaining the two main paradigms of mathematical models most commonly used for representing text data: the statistical and the geometrical approach. Eventually, the third part of the book, Techniques and Applications, addresses general problems in text mining and natural language processing applications such as document categorization, document search, content analysis, summarization, question answering, and conversational systems. This second edition includes updates in line with the recently released "Text Analytics Toolbox" within the MATLAB product and introduces three new chapters and six new sections in existing ones. All descriptions presented are supported with practical examples that are fully reproducible. Further reading, as well as additional exercises and projects, are proposed at the end of each chapter for those readers interested in conducting further experimentation.

This book provides an accessible introduction and practical guidelines to apply asymmetric multidimensional scaling, cluster analysis, and related methods to asymmetric one-mode two-way and three-way asymmetric data. A major objective of this book is to present to applied researchers a set of methods and algorithms for graphical representation and clustering of asymmetric relationships. Data frequently concern measurements of asymmetric relationships between pairs of objects from a given set (e.g., subjects, variables, attributes,...), collected in one or more matrices. Examples abound in many different fields such as psychology, sociology, marketing research, and linguistics and more recently several applications have appeared in technological areas including cybernetics, air traffic control, robotics, and network analysis. The capabilities of the presented algorithms are illustrated by carefully chosen examples and supported by extensive data analyses. A review of the specialized statistical software available for the applications is also provided. This monograph is highly recommended to readers who need a complete and up-to-date reference on methods for asymmetric proximity data analysis.

This compact course is written for the mathematically literate reader who wants to learn to analyze data in a principled fashion. The language of mathematics enables clear exposition that can go quite deep, quite quickly, and naturally supports an axiomatic and inductive approach to data analysis. Starting with a good grounding in probability, the reader moves to statistical inference via topics of great practical importance - simulation and sampling, as well as experimental design and data collection - that are typically displaced from introductory accounts. The core of the book then covers both standard methods and such advanced topics as multiple testing, meta-analysis, and causal inference.

This compact course is written for the mathematically literate reader who wants to learn to analyze data in a principled fashion. The language of mathematics enables clear exposition that can go quite deep, quite quickly, and naturally supports an axiomatic and inductive approach to data analysis. Starting with a good grounding in probability, the reader moves to statistical inference via topics of great practical importance - simulation and sampling, as well as experimental design and data collection - that are typically displaced from introductory accounts. The core of the book then covers both standard methods and such advanced topics as multiple testing, meta-analysis, and causal inference.

Get everything you need to code with ABAP, all in one place! Are you a beginner looking for a refresher on the basics? You'll get an overview of SAP architecture and learn syntax. Already an experienced programmer and looking to improve your ABAP skills? Dive right into modifications and code enhancements. Understand the programming environment and build reports, interfaces, and applications with this complete reference to coding with ABAP! Highlights include: 1) Architecture and environment 2) Syntax, keywords, structures, and tables 3) User interaction 4) Modularization 5) Persistent data 6) Dialog and dynamic programming 7) Reports and forms 8) Interface development 9) Modifications and enhancements 10) Debugging and testing

This book introduces the basic methodologies for successful data analytics. Matrix optimization and approximation are explained in detail and extensively applied to dimensionality reduction by principal component analysis and multidimensional scaling. Diffusion maps and spectral clustering are derived as powerful tools. The methodological overlap between data science and machine learning is emphasized by demonstrating how data science is used for classification as well as supervised and unsupervised learning.

This book discusses all major topics on survey sampling and estimation. It covers traditional as well as advanced sampling methods related to the spatial populations. The book presents real-world applications of major sampling methods and illustrates them with the R software. As a large sample size is not cost-efficient, this book introduces a new method by using the domain knowledge of the negative correlation between the variable of interest and the auxiliary variable in order to control the size of a sample. In addition, the book focuses on adaptive cluster sampling, rank-set sampling and their applications in real life. Advance methods discussed in the book have tremendous applications in ecology, environmental science, health science, forestry, bio-sciences, and humanities. This book is targeted as a text for undergraduate and graduate students of statistics, as well as researchers in various disciplines.