The Equation of Knowledge: From Bayes' Rule to a Unified Philosophy of Science introduces readers to the Bayesian approach to science: teasing out the link between probability and knowledge. The author strives to make this book accessible to a very broad audience, suitable for professionals, students, and academics, as well as the enthusiastic amateur scientist/mathematician. This book also shows how Bayesianism sheds new light on nearly all areas of knowledge, from philosophy to mathematics, science and engineering, but also law, politics and everyday decision-making. Bayesian thinking is an important topic for research, which has seen dramatic progress in the recent years, and has a significant role to play in the understanding and development of AI and Machine Learning, among many other things. This book seeks to act as a tool for proselytising the benefits and limits of Bayesianism to a wider public. Features Presents the Bayesian approach as a unifying scientific method for a wide range of topics Suitable for a broad audience, including professionals, students, and academics Provides a more accessible, philosophical introduction to the subject that is offered elsewhere

Applied Mathematics with Open-source Software: Operational Research Problems with Python and R is aimed at a broad segment of readers who wish to learn how to use open-source software to solve problems in applied mathematics. The book has an innovative structure with 4 sections of two chapters covering a large range of applied mathematical techniques: probabilistic modelling, dynamical systems, emergent behaviour and optimisation. The pairs of chapters in each section demonstrate different families of solution approaches. Each chapter starts with a problem, gives an overview of the relevant theory, shows a solution approach in R and in Python, and finally gives wider context by including a number of published references. This structure will allow for maximum accessibility, with minimal prerequisites in mathematics or programming as well as giving the right opportunities for a reader wanting to delve deeper into a particular topic. Features An excellent resource for scholars of applied mathematics and operational research, and indeed any academics who want to learn how to use open-source software. Offers more general and accessible treatment of the subject than other texts, both in terms of programming language but also in terms of the subjects considered. The R and Python sections purposefully mirror each other so that a reader can read only the section that interests them. An accompanying open-source repository with source files and further examples is posted online at https://bit.ly/3kpoKSd.

Through four editions this popular textbook attracted a loyal readership and widespread use. Students find the book to be concise, accessible, and complete. Instructors find the book to be clear, authoritative, and dependable. The primary goal of this new edition remains the same as in previous editions. It is to make real analysis relevant and accessible to a broad audience of students with diverse backgrounds while also maintaining the integrity of the course. This text aims to be the generational touchstone for the subject and the go-to text for developing young scientists. This new edition continues the effort to make the book accessible to a broader audience. Many students who take a real analysis course do not have the ideal background. The new edition offers chapters on background material like set theory, logic, and methods of proof. The more advanced material in the book is made more apparent. This new edition offers a new chapter on metric spaces and their applications. Metric spaces are important in many parts of the mathematical sciences, including data mining, web searching, and classification of images. The author also revised the material on sequences and series adding examples and exercises that compare convergence tests and give additional tests. The text includes rare topics such as wavelets and applications to differential equations. The level of difficulty moves slowly, becoming more sophisticated in later chapters. Students have commented on the progression as a favorite aspect of the textbook. The author is perhaps the most prolific expositor of upper division mathematics. With over seventy books in print, thousands of students have been taught and learned from his books.

Este texto lider en el mercado ofrece una amplia introduccion a la probabilidad y la estadistica para estudiantes de ingenieria en todas las especialidades. Demostrado, preciso y alabado por sus excelentes ejemplos, probabilidad y estadistica para ingenieria y ciencias evidencia la reputacion de Jay Devore como un autor destacado y lider en la comunidad academica.Devore hace hincapie en los conceptos, modelos, metodologia y aplicaciones en comparacion con el desarrollo matematico riguroso y derivaciones. Ayudado por sus ejemplos vivos y realistas, los estudiantes van mas alla de simplemente aprender acerca de las estadisticas, aprendiendo tambien como poner los metodos estadisticos en uso.

Quantitative Finance with Python: A Practical Guide to Investment Management, Trading and Financial Engineering bridges the gap between the theory of mathematical finance and the practical applications of these concepts for derivative pricing and portfolio management. The book provides students with a very hands-on, rigorous introduction to foundational topics in quant finance, such as options pricing, portfolio optimization and machine learning. Simultaneously, the reader benefits from a strong emphasis on the practical applications of these concepts for institutional investors. Features Useful as both a teaching resource and as a practical tool for professional investors. Ideal textbook for first year graduate students in quantitative finance programs, such as those in master's programs in Mathematical Finance, Quant Finance or Financial Engineering. Includes a perspective on the future of quant finance techniques, and in particular covers some introductory concepts of Machine Learning. Free-to-access repository with Python codes available at www.routledge.com/ 9781032014432.

First published in 1967, this book explores the theme of geographical generalization, or model building. It is composed of five of the chapters from the original Models in Geography, published in 1967. The first chapter broadly outlines this theme and examines the nature and function of generalized statements, ranging from conceptual models to scale models, in a geographical context. The following chapters deal with mixed-system model building in geography, wherein data, techniques and concepts in both physical and human geography are integrated. The book contains chapters on organisms and ecosystems as geographical models as well as spatial patterns in human geography. This text represents a robustly anti-idiographic statement of modern work in one of the major branches of geography.

Algorithim (mathematics) helps in understanding the direct and indirect relationship of plants that exist within it and other environmental factors. This book helps to understand how yield is related to different growth parameters, how growth is influenced by different environmental phenomenon, how best the resources can be used for crop production, etc. The numerical examples in the book guide a student to coordinate the different parameters and understand the subject of Agronomy well. This book is divided into thirteen chapters and covers comprehensively the different agronomic aspects to understand the science of mathematical Agronomy to meet the current and future challenges related to cropping practices.

The field and topic of optimization is not only a very hot topic now, it is morphing into new approaches. Presents a very contemporary approach. Appeal to mathematicians, yet will also find use in computer science and engineering, especially in operations research. Practical approach presents a framework to be used by students and professionals alike to tackle models needed for various applications and solutions.

Drawing upon more than 30 years of experience in working with statistics, Dr. Richard J. Harris has updated A Primer of Multivariate Statistics to provide a model of balance between how-to and why. This classic text covers multivariate techniques with a taste of latent variable approaches. Throughout the book there is a focus on the importance of describing and testing one's interpretations of the emergent variables that are produced by multivariate analysis. This edition retains its conversational writing style while focusing on classical techniques. The book gives the reader a feel for why one should consider diving into more detailed treatments of computer-modeling and latent-variable techniques, such as non-recursive path analysis, confirmatory factor analysis, and hierarchical linear modeling. Throughout the book there is a focus on the importance of describing and testing one's interpretations of the emergent variables that are produced by multivariate analysis.

A new era of international migration has been accompanied by increasingly restrictive immigration controls to manage migration to more developed countries. The consequence has been fewer routes to enter and/or stay in countries in a regularised way and as a result, an increase in the numbers of undocumented migrants. In this situation undocumented migrants, especially in relation to immigration controls and internal security have come to occupy an important role on the policy agenda of many nation states. The control and regulation of undocumented migrants has become an increasingly politicised issue. This edited collection brings together cutting edge scholarly research papers to explore undocumented migration at the international, national and individual levels. Starting with an overview of the literature on undocumented migration this book explores some of the key areas of research and policy in this area. This includes the making of undocumented migrants, the journey and processes, experiences of being undocumented at the individual level, collective action and return. This fascinating book explores the many facets of undocumented migration and of being an undocumented migrant in different geographical contexts that include Europe, Southern Africa, Central America and North America. This book was originally published as a special issue of Ethnic and Racial Studies.

What was the basis for the adoption of mathematics as the primary mode of discourse for describing natural events by a large segment of the philosophical community in the seventeenth century? In answering this question, this book demonstrates that a significant group of philosophers shared the belief that there is no necessary correspondence between external reality and objects of human understanding, which they held to include the objects of mathematical and linguistic discourse. The result is a scholarly reliable, but accessible, account of the role of mathematics in the works of (amongst others) Galileo, Kepler, Descartes, Newton, Leibniz, and Berkeley. This impressive volume will benefit scholars interested in the history of philosophy, mathematical philosophy and the history of mathematics.

An introductory textbook, Logic for Justice covers, in full detail, the language and semantics of both propositional logic and first-order logic. It motivates the study of those logical systems by drawing on social and political issues. Basically, Logic for Justice frames propositional logic and first-order logic as two theories of the distinction between good arguments and bad arguments. And the book explains why, for the purposes of social justice and political reform, we need theories of that distinction. In addition, Logic for Justice is extremely lucid, thorough, and clear. It explains, and motivates, many different features of the formalism of propositional logic and first-order logic, always connecting those features back to real-world issues. Key Features Connects the study of logic to real-world social and political issues, drawing in students who might not otherwise be attracted to the subject. Offers extremely clear and thorough presentations of technical material, allowing students to learn directly from the book without having to rely on instructor explanations. Carefully explains the value of arguing well throughout one’s life, with several discussions about how to argue and how arguments – when done with care – can be helpful personally. Includes examples that appear throughout the entire book, allowing students to see how the ideas presented in the book build on each other. Provides a large and diverse set of problems for each chapter. Teaches logic by connecting formal languages to natural languages with which students are already familiar, making it much easier for students to learn how logic works.

Digital Image Processing with C++ presents the theory of digital image processing, and implementations of algorithms using a dedicated library. Processing a digital image means transforming its content (denoising, stylizing, etc.), or extracting information to solve a given problem (object recognition, measurement, motion estimation, etc.). This book presents the mathematical theories underlying digital image processing, as well as their practical implementation through examples of algorithms implemented in the C++ language, using the free and easy-to-use CImg library. Chapters cover in a broad way the field of digital image processing and proposes practical and functional implementations of each method theoretically described. The main topics covered include filtering in spatial and frequency domains, mathematical morphology, feature extraction and applications to segmentation, motion estimation, multispectral image processing and 3D visualization. Students or developers wishing to discover or specialize in this discipline, teachers and researchers wishing to quickly prototype new algorithms, or develop courses, will all find in this book material to discover image processing or deepen their knowledge in this field.

Access US Census Bureau geographic and demographic data directly within R. Wrangle Census data with tidyverse tools and work with margins of error in the American Community Survey. Make maps and interactive web visualizations with US Census data. Explore Census data with spatial analysis using the sf package. Integrate Census data into spatial and machine learning models.

This textbook intends to be a comprehensive and substantially self-contained two-volume book covering performance, reliability, and availability evaluation subjects. The volumes focus on computing systems, although the methods may also be applied to other systems. The first volume covers Chapter 1 to Chapter 14, whose subtitle is ``Performance Modeling and Background". The second volume encompasses Chapter 15 to Chapter 25 and has the subtitle ``Reliability and Availability Modeling, Measuring and Workload, and Lifetime Data Analysis". This text is helpful for computer performance professionals for supporting planning, design, configuring, and tuning the performance, reliability, and availability of computing systems. Such professionals may use these volumes to get acquainted with specific subjects by looking at the particular chapters. Many examples in the textbook on computing systems will help them understand the concepts covered in each chapter. The text may also be helpful for the instructor who teaches performance, reliability, and availability evaluation subjects. Many possible threads could be configured according to the interest of the audience and the duration of the course. Chapter 1 presents a good number of possible courses programs that could be organized using this text. Volume II is composed of the last two parts. Part III examines reliability and availability modeling by covering a set of fundamental notions, definitions, redundancy procedures, and modeling methods such as Reliability Block Diagrams (RBD) and Fault Trees (FT) with the respective evaluation methods, adopts Markov chains, Stochastic Petri nets and even hierarchical and heterogeneous modeling to represent more complex systems. Part IV discusses performance measurements and reliability data analysis. It first depicts some basic measuring mechanisms applied in computer systems, then discusses workload generation. After, we examine failure monitoring and fault injection, and finally, we discuss a set of techniques for reliability and maintainability data analysis.

*E-statistics provides powerful methods to deal with problems in multivariate inference and analysis *Methods are implemented in R, and readers can immediately apply them using the freely available energy package for R *The proposed book will provide an overview of the existing state-of-the-art in development of energy statistics and an overview of applications. *Background and literature review is valuable for anyone considering further research or application in energy statistics.

This volume is based on the presentations and discussions of a national symposium on "Couples in Conflict" that focused on family issues. A common thread throughout is that constructive conflict and negotiation are beneficial for relationships. Together, the chapters provide a foundation for thinking about creative ways in which our society can work to prevent or minimize destructive couple conflict and to enhance couples' abilities to constructively handle their differences. Divided into four parts, this book: *addresses the societal and bioevolutionary underpinnings of couple conflict; *presents the interpersonal roots of couple conflict and the consequences for individuals and couples; *discusses what effects couple conflict have on children and how individual differences in children moderate these effects; and *outlines the issue of policies and programs that address couple conflict. This book concludes with an essay that pulls these four themes together and points to new directions for research and program efforts.

Provides step-by-step tutorials with clinically relevant examples and data sets to allow the readers to be more interactive with the topics discussed within Provides a relatable context to the examples provided within the text which helps make the topic more easily understood and manageable, thereby making the book popular more accessible to student readers Includes case studies to demonstrate how the statistical test was used to answer a clinically relevant research question

Modern computer-intensive statistical methods play a key role in solving many problems across a wide range of scientific disciplines. Like its bestselling predecessors, the fourth edition of Randomization, Bootstrap and Monte Carlo Methods in Biology illustrates a large number of statistical methods with an emphasis on biological applications. The focus is now on the use of randomization, bootstrapping, and Monte Carlo methods in constructing confidence intervals and doing tests of significance. The text provides comprehensive coverage of computer-intensive applications, with data sets available online. Features Presents an overview of computer-intensive statistical methods and applications in biology Covers a wide range of methods including bootstrap, Monte Carlo, ANOVA, regression, and Bayesian methods Makes it easy for biologists, researchers, and students to understand the methods used Provides information about computer programs and packages to implement calculations, particularly using R code Includes a large number of real examples from a range of biological disciplines Written in an accessible style, with minimal coverage of theoretical details, this book provides an excellent introduction to computer-intensive statistical methods for biological researchers. It can be used as a course text for graduate students, as well as a reference for researchers from a range of disciplines. The detailed, worked examples of real applications will enable practitioners to apply the methods to their own biological data.

Introduction to Political Psychology explores the many psychological patterns that influence individual political behavior. The authors introduce readers to a broad range of theories, concepts, and case studies of political activity, arguing that individuals are driven or motivated to act in accordance with personality characteristics, values, beliefs, and attachments to groups. The book explains many aspects of political behavior-whether seemingly pathological actions or normal decision-making practices, which sometimes work optimally, and sometimes fail. Thoroughly updated throughout, the book examines patterns of political behavior in areas including leadership, group behavior, voting, race, nationalism, terrorism, and war. This edition features coverage of the 2016 election and profiles former U.S. President Donald Trump, while also including updated data on race relations and extremist groups in the United States. Global issues are also considered, with case studies focused on Myanmar and Syria, alongside coverage of social issues including Black Lives Matter and the #MeToo movement. Accessibly written and comprehensive in scope, it is an essential companion for all graduate and upper-level undergraduate students of psychology, political science, and political psychology. It will also be of interest to those in the policy-making community, especially those looking to learn more about the extent to which perceptions, personality, and group dynamics affect the policy-making arena. It is accompanied by a set of online instructor resources.

The author has shown that practically all our laws, principles, and theories are not physically realizable, since they were derived from an empty space paradigm. From which this book is started with the origin of our temporal (t > 0) universe, it shows that temporal subspace is a physically realizable space within our universe. As in contrasted with generally accepted paradigm where time is an independent variable. From which the author has shown that it is not how rigorous mathematics is, but it is the temporal (t > 0) space paradigm determines the physically realizable solution. Although Einstein's relativity and Schroedinger's principle had revolutionized the modern science, this book has shown that both theory and principle are physically non-realizable since they were developed from an empty space paradigm. One of the most important contribution of this book must be the revolutionary idea of our temporal (t > 0) space, for which the author has shown that absolute certainty exists only at the present (t = 0) moment. Where past-time information has no physical substance and future-time represents a physically realizable yet uncertainty. From which the author has shown that all the existent laws, principles, and theories were based on past-time certainties to predict the future, but science is supposed to be approximated. The author has also shown that this is precisely our theoretical science was developed. But time independent laws and principles are not existed within our temporal universe, in view of the author's temporal exclusive principle. By which the author has noted that timeless science has already created a worldwide conspiracy for examples such as superposition principle, qubit information, relativity theory, wormhole travelling and many others. This book has also shown that Heisenberg's uncertainty is an observational principle independent with time, yet within our universe everything changes with time. In this book the author has also noted that micro space behaviors the same as macro space regardless of the particle size. Finally, one of interesting feature is that, that big bang creation was ignited by a self-induced gravitational force instead by time as commonly believed. Nevertheless, everything has a price to pay; a section of time t and an amount of energy E and it is not free. The author has also shown that time is the only variable that cannot be changed. Although we can squeeze a section of time t as small as we wish but we can never able to squeeze t to zero even we have all the needed energy. Nevertheless, this revolutionary book closer to the truth is highly recommended to every scientist and engineer, otherwise we will forever be trapped within the timeless fantasyland of science. This book is intended for cosmologists, particle physicists, astrophysicists, quantum physicists, computer scientists, optical scientists, communication engineers, professors, and students as a reference or a research-oriented book.

First published in 1967, this book explores the theme of geographical generalization, or model building. It is composed of eight of the chapters from the original Models in Geography, published in 1967. The first chapter broadly outlines geographical generalization and examines the nature and function of generalized statements, ranging from conceptual models to scale models, in a geographical context. The following chapter deals with model theory in a wider scientific framework and the rest of the book discusses models of physical systems and information models. The book considers model-type generalizations that are applied in the three fields of geomorphology, meteorology and climatology, and hydrology before focusing on the transference of information and ideas in geography. This text represents a robustly anti-idiographic statement of modern work in one of the major branches of geography.

Gets right to the point with step-by-step guidance on solving physics problems. Covers all topics in standard general physics courses in the same sequence. Keeps learning about physics fun and engaging through the story of dinosaurs being tested on their knowledge for a final challenge (deflecting an asteroid headed to Earth!). Enables the reader to quickly flip through and locate steps needed for a particular problem. Includes tons of easy to follow diagrams and worked solutions.