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Applications to Matrix Calculations, Systems of Equations, Inequalities, and Linear Programming
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Fundamental concepts of mathematical modeling
Modeling is one of the most effective, commonly used tools in engineering and the applied sciences. In this book, the authors deal with mathematical programming models both linear and nonlinear and across a wide range of practical applications.
Whereas other books concentrate on standard methods of analysis, the authors focus on the power of modeling methods for solving practical problems-clearly showing the connection between physical and mathematical realities-while also describing and exploring the main concepts and tools at work. This highly computational coverage includes:
Discussion and implementation of the GAMS programming system Unique coverage of compatibility Illustrative examples that showcase the connection between model and reality Practical problems covering a wide range of scientific disciplines, as well as hundreds of examples and end-of-chapter exercises Real-world applications to probability and statistics, electrical engineering, transportation systems, and moreBuilding and Solving Mathematical Programming Models in Engineering and Science is practically suited for use as a professional reference for mathematicians, engineers, and applied or industrial scientists, while also tutorial and illustrative enough for advanced students in mathematics or engineering.
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Barry Arnold has made fundamental contributions to many different areas of statistics, including distribution theory, Bayesian inference, multivariate analysis, bounds and orderings, and characterization problems. Organized to honor Arnold’s significant contributions to the field, this volume is an outgrowth of the "International Conference on Distribution Theory, Order Statistics, and Inference," held at the University of Cantabria, Santander, Spain.
Several distinguished and active researchers highlight some of the recent developments in statistical distribution theory, order statistics and their properties, as well as inferential methods associated with them. Applications to survival analysis, reliability, quality control, and environmental problems are emphasized.
This comprehensive reference work will serve the statistical and applied mathematics communities, as well as practitioners, researchers, and graduate students in applied probability and statistics, reliability engineering, and biostatistics.
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A Neural-Based Paradigm
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