Bayesian Inference and Maximum Entropy Methods in Science and Engineering

A multitude of processes in hydrology and environmental engineering are either random or entail random components which are characterized by random variables. These variables are described by frequency distributions. This book provides an overview of different systems of frequency distributions, their properties, and applications to the fields of water resources and environmental engineering. A variety of systems are covered, including the Pearson system, Burr system, and systems commonly applied in economics, such as the D'Addario, Dagum, Stoppa, and Esteban systems. The latter chapters focus on the Singh system and the frequency distributions deduced from Bessel functions, maximum entropy theory, and the transformations of random variables. The final chapter introduces the genetic theory of frequency distributions. Using real-world data, this book provides a valuable reference for researchers, graduate students, and professionals interested in frequency analysis.

The Third Edition of this popular and engaging text consolidates the interdisciplinary streams of cognitive science to present a unified narrative of cognitive science as a discipline in its own right. It teaches students to apply the techniques and theories of the cognitive scientist's 'toolkit' - the vast range of methods and tools that cognitive scientists use to study the mind. Thematically organized, Cognitive Science underscores the problems and solutions of cognitive science rather than more narrowly examining individually the subjects that contribute to it - psychology, neuroscience, linguistics, and so on. The generous use of examples, illustrations, and applications demonstrates how theory is applied to unlock the mysteries of the human mind. Drawing upon cutting-edge research, the text has been substantially revised, with new material on Bayesian approaches to the mind and on deep learning. An extensive on-line set of resources is available to aid instructors and students al

The Third Edition of this popular and engaging text consolidates the interdisciplinary streams of cognitive science to present a unified narrative of cognitive science as a discipline in its own right. It teaches students to apply the techniques and theories of the cognitive scientist's 'toolkit' - the vast range of methods and tools that cognitive scientists use to study the mind. Thematically organized, Cognitive Science underscores the problems and solutions of cognitive science rather than more narrowly examining individually the subjects that contribute to it - psychology, neuroscience, linguistics, and so on. The generous use of examples, illustrations, and applications demonstrates how theory is applied to unlock the mysteries of the human mind. Drawing upon cutting-edge research, the text has been substantially revised, with new material on Bayesian approaches to the mind and on deep learning. An extensive on-line set of resources is available to aid instructors and students al

Cluster analysis finds groups in data automatically. Most methods have been heuristic and leave open such central questions as: how many clusters are there? Which method should I use? How should I handle outliers? Classification assigns new observations to groups given previously classified observations, and also has open questions about parameter tuning, robustness and uncertainty assessment. This book frames cluster analysis and classification in terms of statistical models, thus yielding principled estimation, testing and prediction methods, and sound answers to the central questions. It builds the basic ideas in an accessible but rigorous way, with extensive data examples and R code; describes modern approaches to high-dimensional data and networks; and explains such recent advances as Bayesian regularization, non-Gaussian model-based clustering, cluster merging, variable selection, semi-supervised and robust classification, clustering of functional data, text and images, and co-cl

Gain insight into essential data science skills in a holistic manner using data engineering and associated scalable computational methods. This book covers the most popular Python 3 frameworks for bot

Covering detailed discussion of fundamental concepts of economics, the textbook commences with comprehensive explanation of theory of consumer behavior, utility maximization and optimal choice, profit function, cost minimization and cost function. The textbook covers methods including present worth method, future worth method, annual worth method, internal rate of return method, explicit re-investment rate of return method and payout method useful for studying economic studies. A chapter on value engineering discusses important topics such as function analysis systems techniques, the value index, value measurement techniques, innovative phase and constraints analysis in depth. It facilitates the understanding of the concepts through illustrations and solved problems. This text is the ideal resource for Indian undergraduate engineering students in the fields of mechanical engineering, computer science and engineering and electronics engineering for a course on engineering economics/engi

Geraci offers an investigation into the intersection of religion, science, and technology in scientific and engineering communities in India. Using historical and ethnographic methods, Geraci explores

A. W. F. Edwards is one of the most influential mathematical geneticists in the history of the discipline. One of the last students of R. A. Fisher, Edwards pioneered the statistical analysis of phylogeny in collaboration with L. L. Cavalli-Sforza, and helped establish Fisher's concept of likelihood as a standard of statistical and scientific inference. In this book, edited by philosopher of science Rasmus Grønfeldt Winther, Edwards's key papers are assembled alongside commentaries by leading scientists, discussing Edwards's influence on their own research and on thinking in their field overall. In an extensive interview with Winther, Edwards offers his thoughts on his contributions, their legacy, and the context in which they emerged. This book is a resource both for anyone interested in the history and philosophy of genetics, statistics, and science, and for scientists seeking to develop new algorithmic and statistical methods for understanding the genetic relationships between and a

Mounting failures of replication in social and biological sciences give a new urgency to critically appraising proposed reforms. This book pulls back the cover on disagreements between experts charged with restoring integrity to science. It denies two pervasive views of the role of probability in inference: to assign degrees of belief, and to control error rates in a long run. If statistical consumers are unaware of assumptions behind rival evidence reforms, they can't scrutinize the consequences that affect them (in personalized medicine, psychology, etc.). The book sets sail with a simple tool: if little has been done to rule out flaws in inferring a claim, then it has not passed a severe test. Many methods advocated by data experts do not stand up to severe scrutiny and are in tension with successful strategies for blocking or accounting for cherry picking and selective reporting. Through a series of excursions and exhibits, the philosophy and history of inductive inference come ali

Mounting failures of replication in social and biological sciences give a new urgency to critically appraising proposed reforms. This book pulls back the cover on disagreements between experts charged with restoring integrity to science. It denies two pervasive views of the role of probability in inference: to assign degrees of belief, and to control error rates in a long run. If statistical consumers are unaware of assumptions behind rival evidence reforms, they can't scrutinize the consequences that affect them (in personalized medicine, psychology, etc.). The book sets sail with a simple tool: if little has been done to rule out flaws in inferring a claim, then it has not passed a severe test. Many methods advocated by data experts do not stand up to severe scrutiny and are in tension with successful strategies for blocking or accounting for cherry picking and selective reporting. Through a series of excursions and exhibits, the philosophy and history of inductive inference come ali

The material in this book attempts to address mathematical calculations common to both the environmental science and engineering professionals. The book provides the reader with nearly 100 solved illu

"This book captures the latest research and innovations and case studies in the field of software development risk management. A number of tools, methods, processes alongside theoretical concepts have

Applied Numerical Methods with MATLAB is written for students who want to learn and apply numerical methods in order to solve problems in engineering and science. As such, the methods are motivated by

"This book presents work on biomimetic techniques in the field of research information and knowledge management. It also includes conclusions on new methods derived from nature that have been solution

This introduction to finite difference and finite element methods is aimed at graduate students who need to solve differential equations. The prerequisites are few (basic calculus, linear algebra, and ODEs) and so the book will be accessible and useful to readers from a range of disciplines across science and engineering. Part I begins with finite difference methods. Finite element methods are then introduced in Part II. In each part, the authors begin with a comprehensive discussion of one-dimensional problems, before proceeding to consider two or higher dimensions. An emphasis is placed on numerical algorithms, related mathematical theory, and essential details in the implementation, while some useful packages are also introduced. The authors also provide well-tested MATLAB® codes, all available online.

This introduction to finite difference and finite element methods is aimed at graduate students who need to solve differential equations. The prerequisites are few (basic calculus, linear algebra, and ODEs) and so the book will be accessible and useful to readers from a range of disciplines across science and engineering. Part I begins with finite difference methods. Finite element methods are then introduced in Part II. In each part, the authors begin with a comprehensive discussion of one-dimensional problems, before proceeding to consider two or higher dimensions. An emphasis is placed on numerical algorithms, related mathematical theory, and essential details in the implementation, while some useful packages are also introduced. The authors also provide well-tested MATLAB® codes, all available online.

Federal Data Science serves as a guide for federal software engineers, government analysts, economists, researchers, data scientists, and engineering managers in deploying data analytics methods to go

This volume provides a comprehensive introduction to mHealth technology and is accessible to technology-oriented researchers and practitioners with backgrounds in computer science, engineering, statis

Ultrasonic spectroscopy is a technique widely used in solid-state physics, materials science, and geology that utilizes acoustic waves to determine fundamental physical properties of materials, such as their elasticity and mechanical energy dissipation. This book provides complete coverage of the main issues relevant to the design, analysis, and interpretation of ultrasonic experiments. Topics including elasticity, acoustic waves in solids, ultrasonic loss, and the relation of elastic constants to thermodynamic potentials are covered in depth. Modern techniques and experimental methods including resonant ultrasound spectroscopy, digital pulse-echo, and picosecond ultrasound are also introduced and reviewed. This self-contained book includes extensive background theory and is accessible to students new to the field of ultrasonic spectroscopy, as well as to graduate students and researchers in physics, engineering, materials science, and geophysics.

Reflecting the rising popularity of research that combines qualitative and quantitative social science, Multi-Method Social Science provides the first systematic guide to designing multi-method research. It argues that methods can be productively combined using the framework of integrative multi-method research, with one method used to carry out a final causal inference, and methods from other traditions used to test the key assumptions involved in that causal inference. In making this argument, Jason Seawright considers a wide range of statistical tools including regression, matching, and natural experiments. The book also discusses qualitative tools including process tracing, the use of causal process observations, and comparative case study research. Along the way, the text develops over a dozen multi-method designs to test key assumptions about social science causation.

Reflecting the rising popularity of research that combines qualitative and quantitative social science, Multi-Method Social Science provides the first systematic guide to designing multi-method research. It argues that methods can be productively combined using the framework of integrative multi-method research, with one method used to carry out a final causal inference, and methods from other traditions used to test the key assumptions involved in that causal inference. In making this argument, Jason Seawright considers a wide range of statistical tools including regression, matching, and natural experiments. The book also discusses qualitative tools including process tracing, the use of causal process observations, and comparative case study research. Along the way, the text develops over a dozen multi-method designs to test key assumptions about social science causation.

This book covers finite element methods for several typical eigenvalues that arise from science and engineering. Both theory and implementation are covered in depth at the graduate level. The backgrou

This work gathers international contributors in computer science, information systems, telecommunication engineering, and information technology to present the latest research in complex network analy

Most existing textbooks on this subject were written either for mathematics or engineering students and do not address the unique situation of computer science students, who have some background in di

Optimization is used to determine the most appropriate value of variables under given conditions. The primary focus of using optimisation techniques is to measure the maximum or minimum value of a function depending on the circumstances. This book discusses problem formulation and problem solving with the help of algorithms such as secant method, quasi-Newton method, linear programming and dynamic programming. It also explains important chemical processes such as fluid flow systems, heat exchangers, chemical reactors and distillation systems using solved examples. The book begins by explaining the fundamental concepts followed by an elucidation of various modern techniques including trust-region methods, Levenberg–Marquardt algorithms, stochastic optimization, simulated annealing and statistical optimization. It studies the multi-objective optimization technique and its applications in chemical engineering and also discusses the theory and applications of various optimization software

This authoritative work provides an in-depth treatment of state space methods, with a range of applications in neural and clinical data. Advanced and state-of-the-art research topics are detailed, including topics in state space analyses, maximum likelihood methods, variational Bayes, sequential Monte Carlo, Markov chain Monte Carlo, nonparametric Bayesian, and deep learning methods. Details are provided on practical applications in neural and clinical data, whether this is characterising time series data from neural spike trains recorded from the rat hippocampus, the primate motor cortex, or the human EEG, MEG or fMRI, or physiological measurements of heartbeats or blood pressures. With real-world case studies of neuroscience experiments and clinical data sets, and written by expert authors from across the field, this is an ideal resource for anyone working in neuroscience and physiological data analysis.

This book introduces in a systematic manner a general nonparametric theory of statistics on manifolds, with emphasis on manifolds of shapes. The theory has important and varied applications in medical diagnostics, image analysis, and machine vision. An early chapter of examples establishes the effectiveness of the new methods and demonstrates how they outperform their parametric counterparts. Inference is developed for both intrinsic and extrinsic Fréchet means of probability distributions on manifolds, then applied to shape spaces defined as orbits of landmarks under a Lie group of transformations - in particular, similarity, reflection similarity, affine and projective transformations. In addition, nonparametric Bayesian theory is adapted and extended to manifolds for the purposes of density estimation, regression and classification. Ideal for statisticians who analyze manifold data and wish to develop their own methodology, this book is also of interest to probabilists, mathematicia

Control volume finite element methods (CVFEM) bridge the gap between finite difference and finite element methods, using the advantages of both methods for simulation of multi-physics problems in comp

The description of many interesting phenomena in science and engineering leads to infinite-dimensional minimization or evolution problems that define nonlinear partial differential equations. While th

Learn how to plan for success with this hands-on guide to conducting high-quality engineering research. Plan and implement your next project for maximum impact: step-by-step instructions cover every stage in engineering research, from the identification of an appropriate research topic through to the successful presentation of results. Improve your research outcomes: discover essential tools and methods for producing high-quality, rigorous research, including statistical analysis, survey design, and optimisation techniques. Research with purpose and direction: clear explanations, real-world examples, and over 50 customisable end-of-chapter exercises, all written with the practical and ethical considerations of engineering in mind. A unique engineering perspective: written especially for engineers, and relevant across all engineering disciplines, this is the ideal book for graduate students, undergraduates, and new academics looking to launch their research careers.

Learn how to plan for success with this hands-on guide to conducting high-quality engineering research. Plan and implement your next project for maximum impact: step-by-step instructions cover every stage in engineering research, from the identification of an appropriate research topic through to the successful presentation of results. Improve your research outcomes: discover essential tools and methods for producing high-quality, rigorous research, including statistical analysis, survey design, and optimisation techniques. Research with purpose and direction: clear explanations, real-world examples, and over 50 customisable end-of-chapter exercises, all written with the practical and ethical considerations of engineering in mind. A unique engineering perspective: written especially for engineers, and relevant across all engineering disciplines, this is the ideal book for graduate students, undergraduates, and new academics looking to launch their research careers.

This volume contains thirteen articles on advances in applied mathematics and computing methods for engineering problems. Six papers are on optimization methods and algorithms with emphasis on problem

Taking a broad approach, this book explores recent developments in statistics that have impacted actuarial science. It covers inference, Bayesian methods, statistical learning, spatial analysis, and e

Graph theory gained initial prominence in science and engineering through its strong links with matrix algebra and computer science. Moreover, the structure of the mathematics is well suited to that o

Mobile Robotics offers comprehensive coverage of the essentials of the field suitable for both students and practitioners. Adapted from Alonzo Kelly's graduate and undergraduate courses, the content of the book reflects current approaches to developing effective mobile robots. Professor Kelly adapts principles and techniques from the fields of mathematics, physics and numerical methods to present a consistent framework in a notation that facilitates learning and highlights relationships between topics. This text was developed specifically to be accessible to senior level undergraduates in engineering and computer science, and includes supporting exercises to reinforce the lessons of each section. Practitioners will value Kelly's perspectives on practical applications of these principles. Complex subjects are reduced to implementable algorithms extracted from real systems wherever possible, to enhance the real-world relevance of the text.

Distributions in the Physical and Engineering Sciences is a comprehensive exposition on analytic methods for solving science and engineering problems which is written from the unifying viewpoint of di

Fundamentals of Matrix-Analytic Methods targets advanced-level students in mathematics, engineering and computer science. It focuses on the fundamental parts of Matrix-Analytic Methods, Phase-Type Dis

The orthodontic literature includes many articles on basic science and engineering research techniques, but clinicians are typically unfamiliar with the principles underlying these techniques and may

This 2004 volume offers a broad overview of developments in the theory and applications of state space modeling. With fourteen chapters from twenty-three contributors, it offers a unique synthesis of state space methods and unobserved component models that are important in a wide range of subjects, including economics, finance, environmental science, medicine and engineering. The book is divided into four sections: introductory papers, testing, Bayesian inference and the bootstrap, and applications. It will give those unfamiliar with state space models a flavour of the work being carried out as well as providing experts with valuable state of the art summaries of different topics. Offering a useful reference for all, this accessible volume makes a significant contribution to the literature of this discipline.

This book introduces in a systematic manner a general nonparametric theory of statistics on manifolds, with emphasis on manifolds of shapes. The theory has important and varied applications in medical diagnostics, image analysis, and machine vision. An early chapter of examples establishes the effectiveness of the new methods and demonstrates how they outperform their parametric counterparts. Inference is developed for both intrinsic and extrinsic Fréchet means of probability distributions on manifolds, then applied to shape spaces defined as orbits of landmarks under a Lie group of transformations - in particular, similarity, reflection similarity, affine and projective transformations. In addition, nonparametric Bayesian theory is adapted and extended to manifolds for the purposes of density estimation, regression and classification. Ideal for statisticians who analyze manifold data and wish to develop their own methodology, this book is also of interest to probabilists, mathematicia