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Provides remediation for students who are rusty in their basic computational skills. Helps break the cycle of failure;
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development of statistical theory. At the same time, the computational methods used in statistical work span much of computer science. Elements;
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Suitable for a compact course or self-study, Computational Statistics: An Introduction to R illustrates how to use the freely available R;
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Computational statistics and statistical computing are two areas that employ computational, graphical, and numerical approaches to solve;
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This textbook on computational statistics presents tools and concepts of univariate and multivariate statistical data analysis with a;
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Basic Statistics for Social Research offers an introduction to core general statistical concepts and methods. It covers procedural aspects;
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of computational arithmetic and its influence on computational statistics, numerical algorithms in statistical application software, basics of computer;
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Bayesian statistics and concepts and is highly focused on the computational aspects of Bayesian data analysis of photon-limited data acquired;
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Bayesian statistics and concepts and is highly focused on the computational aspects of Bayesian data analysis of photon-limited data acquired;
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in computational molecular biology and bioinformatics. It contains basic molecular biology concepts, tools, techniques and ways to measure sequence;
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With the rapidly advancing fields of Data Analytics and Computational Statistics, it's important to keep up with current trends;
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In this book wedescribe the basic elements of present computational technologies that use the algorithmic languages C/C++. The emphasis;
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With an emphasis on problem solving, this book introduces the basic principles and fundamental concepts of computational modeling. It;
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Computational inference is based on an approach to statistical methods that uses modern computational power to simulate distributional;
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Computational inference is based on an approach to statistical methods that uses modern computational power to simulate distributional;
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results are values of maximum probability or of averaging. This introduction to statistical physics concentrates on the basic principles, and;
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This textbook presents basic and advanced computational physics in a very didactic style. It contains very-well-presented and simple;
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programming concepts in the context of practical computational problems. The book will be of great interest for all specialists working on;
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on the practical applications of statistical methods in various disciplines, using machine learning and other computational methods. The book;
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like to introduce a more modern (and a more logical) approach in a basic course in statistics." Journal of the American Statistical Association;
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use in data analysis. Basic Computational Techniques for Data Analysis illustrates the concepts used in economic and financial;
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development of efficient and appropriate tools in computational statistics integrated with biological knowledge and computational algorithms. This;
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development of efficient and appropriate tools in computational statistics integrated with biological knowledge and computational algorithms. This;
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concepts of basic statistics, with a focus on solving biomedical problems. Readers will learn how to understand the fundamental concepts;
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It is generally accepted that training in statistics must include some exposure to the mechanics of computational statistics. This learning;
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R and R-Commander as computational tools. The book is ideal for instructors of basic statistics for biologists and other health scientists;
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into three basic parts: the first includes elements of probability theory, the second introduces readers to the basics of descriptive and;
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