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This volume is an eclectic mix of applications of Monte Carlo methods in many fields of research should not be surprising, because of the ubiquitous use of these methods in many fields of human endeavor. In an attempt to focus attention on a manageable set of applications, the main thrust of this book is to emphasize applications of Monte Carlo simulation methods in biology and medicine.
Biology. --- Life sciences --- Life (Biology) --- Natural history --- Mathematical & statistical software
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Model Predictive Control (MPC) is used to solve challenging multivariable-constrained control problems. MPC systems are successfully applied in many different branches of industry. The MPC ToolboxTM of MATLAB®/Simulink® provides powerful tools for industrial MPC application, but also for education and research at technical universities. This book gives an overview of the basic ideas and advantages of the MPC concept. It shows how MPC systems can be designed, tuned, and simulated using the MPC Toolbox. Selected process engineering benchmark examples are used to demonstrate typical design approaches and help deepen the understanding of MPC technologies. The book is aimed at engineers in industry interested in the development and application of MPC systems, as well as students of different technical disciplines seeking an introduction into this field.
Computers / Mathematical & Statistical Software --- Computers --- Automatic computers --- Automatic data processors --- Computer hardware --- Computing machines (Computers) --- Electronic brains --- Electronic calculating-machines --- Electronic computers --- Hardware, Computer --- Computer systems --- Cybernetics --- Machine theory --- Calculators --- Cyberspace --- Mathematical & Statistical Software
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This open access book provides a wealth of hands-on examples that illustrate how hyperparameter tuning can be applied in practice and gives deep insights into the working mechanisms of machine learning (ML) and deep learning (DL) methods. The aim of the book is to equip readers with the ability to achieve better results with significantly less time, costs, effort and resources using the methods described here. The case studies presented in this book can be run on a regular desktop or notebook computer. No high-performance computing facilities are required. The idea for the book originated in a study conducted by Bartz & Bartz GmbH for the Federal Statistical Office of Germany (Destatis). Building on that study, the book is addressed to practitioners in industry as well as researchers, teachers and students in academia. The content focuses on the hyperparameter tuning of ML and DL algorithms, and is divided into two main parts: theory (Part I) and application (Part II). Essential topics covered include: a survey of important model parameters; four parameter tuning studies and one extensive global parameter tuning study; statistical analysis of the performance of ML and DL methods based on severity; and a new, consensus-ranking-based way to aggregate and analyze results from multiple algorithms. The book presents analyses of more than 30 hyperparameters from six relevant ML and DL methods, and provides source code so that users can reproduce the results. Accordingly, it serves as a handbook and textbook alike.
Artificial intelligence --- Machine learning --- Mathematical & statistical software --- Mathematical physics --- Hyperparameter Tuning --- Hyperparameters --- Tuning --- Deep Neural Networks --- Reinforcement Learning --- Machine Learning
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This book is designed to provide a comprehensive introduction to R programming for data analysis, manipulation and presentation. It covers fundamental data structures such as vectors, matrices, arrays and lists, along with techniques for exploratory data analysis, data transformation and manipulation. The book explains basic statistical concepts and demonstrates their implementation using R, including descriptive statistics, graphical representation of data, probability, popular probability distributions and hypothesis testing. It also explores linear and non-linear modeling, model selection and diagnostic tools in R. The book also covers flow control and conditional calculations by using ‘‘if’’ conditions and loops and discusses useful functions and resources for further learning. It provides an extensive list of functions grouped according to statistics classification, which can be helpful for both statisticians and R programmers. The use of different graphic devices, high-level and low-level graphical functions and adjustment of parameters are also explained. Throughout the book, R commands, functions and objects are printed in a different font for easy identification. Common errors, warnings and mistakes in R are also discussed and classified with explanations on how to prevent them.
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This book offers a thorough understanding of Hierarchical Archimedean Copulas (HACs) and their practical applications. It covers the basics of copulas, explores the Archimedean family, and delves into the specifics of HACs, including their fundamental properties. The text also addresses sampling algorithms, HAC parameter estimation, and structure, and highlights temporal models with applications in finance and economics. The final chapter introduces R, MATLAB, and Octave toolboxes for copula modeling, enabling students, researchers, data scientists, and practitioners to model complex dependence structures and make well-informed decisions across various domains.
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This book shows you how to analyze data sets systematically and to use the R Commander 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. R and the R Commander functions used are explained in detail – the procedure can be easily transferred to other data sets. The book thus provides a simple introduction to professional and free statistical software. 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 the R Commander. 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 dem R Commander 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. The Author Prof. Dr. Franz Kronthaler is professor of economics and statistics at the University of Applied Sciences Graubünden FHGR. He has been working as an empirical economic researcher for more than 20 years, has conducted numerous research projects and published papers. For many years, he has been teaching students of various disciplines in applied statistics and the advanced methods of data analysis at Bachelor and Master level. The translation was done with the help of artificial intelligence. A subsequent human revision was done primarily in terms of content.
Statistics --- Statistics. --- Quantitative research. --- Statistical Software. --- Applied Statistics. --- Data Analysis and Big Data. --- Computer programs. --- Data processing.
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681.3*G3 --- 519.217 --- Probability and statistics: probabilistic algorithms (including Monte Carlo)random number generation statistical computing statistical software (Mathematics of computing) --- Markov processes --- 519.217 Markov processes --- 681.3*G3 Probability and statistics: probabilistic algorithms (including Monte Carlo)random number generation statistical computing statistical software (Mathematics of computing) --- 681.3*G3 Probability and statistics: probabilistic algorithms (including Monte Carlo);random number generation; statistical computing; statistical software (Mathematics of computing) --- Probability and statistics: probabilistic algorithms (including Monte Carlo);random number generation; statistical computing; statistical software (Mathematics of computing) --- Stochastic processes. --- Stochastic processes --- Processus stochastiques
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Mathematical statistics --- Basic Sciences. Statistics --- Data processing. --- Mathematical Statistics. --- Probability and statistics: probabilistic algorithms (including Monte Carlo);random number generation; statistical computing; statistical software (Mathematics of computing) --- -Probability and statistics: probabilistic algorithms (including Monte Carlo);random number generation; statistical computing; statistical software (Mathematics of computing) --- 681.3*G3 Probability and statistics: probabilistic algorithms (including Monte Carlo);random number generation; statistical computing; statistical software (Mathematics of computing) --- -681.3*G3 Probability and statistics: probabilistic algorithms (including Monte Carlo);random number generation; statistical computing; statistical software (Mathematics of computing) --- 681.3*G3 --- Data processing --- S-Plus. --- SPlus --- Programming --- Statistique mathématique --- Informatique
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681.3*G3 --- #SBIB:303H4 --- 681.3*G3 Probability and statistics: probabilistic algorithms (including Monte Carlo)random number generation statistical computing statistical software (Mathematics of computing) --- Probability and statistics: probabilistic algorithms (including Monte Carlo)random number generation statistical computing statistical software (Mathematics of computing) --- Informatica in de sociale wetenschappen --- 681.3*G3 Probability and statistics: probabilistic algorithms (including Monte Carlo);random number generation; statistical computing; statistical software (Mathematics of computing) --- Probability and statistics: probabilistic algorithms (including Monte Carlo);random number generation; statistical computing; statistical software (Mathematics of computing) --- Social sciences --- Statistical methods --- Computer programs --- SPSS for Windows --- Social sciences - Statistical methods - Computer programs
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Informatica 681.3 --- Statistische analyse 519.23 --- #SERV: inv. Leuven --- 681.3*G3 --- 681.3*G3 Probability and statistics: probabilistic algorithms (including Monte Carlo)random number generation statistical computing statistical software (Mathematics of computing) --- Probability and statistics: probabilistic algorithms (including Monte Carlo)random number generation statistical computing statistical software (Mathematics of computing) --- Mathematical statistics --- 681.3*G3 Probability and statistics: probabilistic algorithms (including Monte Carlo);random number generation; statistical computing; statistical software (Mathematics of computing) --- Probability and statistics: probabilistic algorithms (including Monte Carlo);random number generation; statistical computing; statistical software (Mathematics of computing) --- Data processing --- SAS (Computer file) --- Statistical analysis system --- SAS system
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