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Science and engineering students depend heavily on concepts of mathematical modeling. In an age where almost everything is done on a computer, author Clive Dym believes that students need to understand and ""own"" the underlying mathematics that computers are doing on their behalf. His goal for Principles of Mathematical Modeling, Second Edition, is to engage the student reader in developing a foundational understanding of the subject that will serve them well into their careers. The first half of the book begins with a clearly defined set of modeling principles, and then intro

Mathematical models. --- Mathematical models --- Operations Research --- Civil & Environmental Engineering --- Engineering & Applied Sciences --- Models, Mathematical --- Simulation methods

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Mathematical statistics --- 519.22 --- Mathematics --- Statistical inference --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- Statistical theory. Statistical models. Mathematical statistics in general --- Statistical methods --- 519.22 Statistical theory. Statistical models. Mathematical statistics in general

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Incorporating new and updated information, this second edition of THE bestselling text in Bayesian data analysis continues to emphasize practice over theory, describing how to conceptualize, perform, and critiques statistical analysis from a Bayesian perspective. Changes in the new edition include: added material on how Bayesian methods are connected to other approaches, stronger focus on MCMC, added chapter on further computation topics, more examples, and additional chapters on current models for Bayesian data analysis such as equation models, generalized linear mixed models, and more. The book is an introductory text and a reference for working scientists throughout their professional life.

Mathematical statistics --- Bayesian statistical decision theory --- 519.22 --- 57.087.1 --- 519.542 --- Bayesian statistical decision theory. --- Bayes' solution --- Bayesian analysis --- Statistical decision --- Statistical theory. Statistical models. Mathematical statistics in general --- Biometry. Statistical study and treatment of biological data --- Methoden en technieken --- statistiek --- 57.087.1 Biometry. Statistical study and treatment of biological data --- 519.22 Statistical theory. Statistical models. Mathematical statistics in general --- statistiek. --- Statistiek.

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Over the past decade there has been an increasing demand for suitable material in the area of mathematical modelling as applied to science, engineering, business and management. Recent developments in computer technology and related software have provided the necessary tools of increasing power and sophistication which have significant implications for the use and role of mathematical modelling in the above disciplines. In the past, traditional methods have relied heavily on expensive experimentation and the building of scaled models, but now a more flexible and cost effective approach is available through greater use of mathematical modelling and computer simulation. In particular, developments in computer algebra, symbolic manipulation packages and user friendly software packages for large scale problems, all have important implications in both the teaching of mathematical modelling and, more importantly, its use in the solution of real world problems. Many textbooks have been published which cover the art and techniques of modelling as well as specific mathematical modelling techniques in specialist areas within science and business. In most of these books the mathematical material tends to be rather tailor made to fit in with a one or two semester course for teaching students at the undergraduate or postgraduate level, usually the former. This textbook is quite different in that it is intended to build on and enhance students’ modelling skills using a combination of case studies and projects.

Mathematical models. --- Mathematical models --- Engineering. --- Mechanics. --- Numerical analysis. --- Algorithms. --- Mathematical Modeling and Industrial Mathematics. --- Engineering, general. --- Classical Mechanics. --- Numeric Computing. --- Algorism --- Algebra --- Arithmetic --- Mathematical analysis --- Classical mechanics --- Newtonian mechanics --- Physics --- Dynamics --- Quantum theory --- Construction --- Industrial arts --- Technology --- Models, Mathematical --- Simulation methods --- Foundations

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In this new edition, the fundamental material on classical linear aeroelasticity has been revised. Also new material has been added describing recent results on the research frontiers dealing with nonlinear aeroelasticity as well as major advances in the modelling of unsteady aerodynamic flows using the methods of computational fluid dynamics and reduced order modeling techniques. New chapters on aeroelasticity in turbomachinery and aeroelasticity and the latter chapters for a more advanced course, a graduate seminar or as a reference source for an entrée to the research literature.

Aeroelasticity. --- Aéroélasticité --- Aerodynamics. --- Aerodynamics --- Elastic waves --- Elasticity --- Aerodynamics, Subsonic --- Airplanes --- Streamlining --- Subsonic aerodynamics --- Dynamics --- Fluid dynamics --- Gas dynamics --- Pneumatics --- Aeronautics --- Wind tunnels --- Aéroélasticité. --- Vibration. --- Dynamical systems. --- Dynamics. --- Automotive engineering. --- Mechanical engineering. --- Civil engineering. --- Mathematical models. --- Vibration, Dynamical Systems, Control. --- Automotive Engineering. --- Mechanical Engineering. --- Civil Engineering. --- Mathematical Modeling and Industrial Mathematics. --- Models, Mathematical --- Simulation methods --- Engineering --- Public works --- Engineering, Mechanical --- Machinery --- Steam engineering --- Dynamical systems --- Kinetics --- Mathematics --- Mechanics, Analytic --- Force and energy --- Mechanics --- Physics --- Statics --- Cycles --- Sound