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the probability companion for engineering and computer science: The Probability Companion for Engineering and Computer Science Adam Prügel-Bennett, 2020-01-23 This friendly guide is the companion you need to convert pure mathematics into understanding and facility with a host of probabilistic tools. The book provides a high-level view of probability and its most powerful applications. It begins with the basic rules of probability and quickly progresses to some of the most sophisticated modern techniques in use, including Kalman filters, Monte Carlo techniques, machine learning methods, Bayesian inference and stochastic processes. It draws on thirty years of experience in applying probabilistic methods to problems in computational science and engineering, and numerous practical examples illustrate where these techniques are used in the real world. Topics of discussion range from carbon dating to Wasserstein GANs, one of the most recent developments in Deep Learning. The underlying mathematics is presented in full, but clarity takes priority over complete rigour, making this text a starting reference source for researchers and a readable overview for students. |
the probability companion for engineering and computer science: Probability and Statistics with Reliability, Queuing, and Computer Science Applications Kishor S. Trivedi, 2016-07-11 An accessible introduction to probability, stochastic processes, and statistics for computer science and engineering applications Second edition now also available in Paperback. This updated and revised edition of the popular classic first edition relates fundamental concepts in probability and statistics to the computer sciences and engineering. The author uses Markov chains and other statistical tools to illustrate processes in reliability of computer systems and networks, fault tolerance, and performance. This edition features an entirely new section on stochastic Petri nets—as well as new sections on system availability modeling, wireless system modeling, numerical solution techniques for Markov chains, and software reliability modeling, among other subjects. Extensive revisions take new developments in solution techniques and applications into account and bring this work totally up to date. It includes more than 200 worked examples and self-study exercises for each section. Probability and Statistics with Reliability, Queuing and Computer Science Applications, Second Edition offers a comprehensive introduction to probability, stochastic processes, and statistics for students of computer science, electrical and computer engineering, and applied mathematics. Its wealth of practical examples and up-to-date information makes it an excellent resource for practitioners as well. An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department. |
the probability companion for engineering and computer science: Introduction to Probability Dimitri Bertsekas, John N. Tsitsiklis, 2008-07-01 An intuitive, yet precise introduction to probability theory, stochastic processes, statistical inference, and probabilistic models used in science, engineering, economics, and related fields. This is the currently used textbook for an introductory probability course at the Massachusetts Institute of Technology, attended by a large number of undergraduate and graduate students, and for a leading online class on the subject. The book covers the fundamentals of probability theory (probabilistic models, discrete and continuous random variables, multiple random variables, and limit theorems), which are typically part of a first course on the subject. It also contains a number of more advanced topics, including transforms, sums of random variables, a fairly detailed introduction to Bernoulli, Poisson, and Markov processes, Bayesian inference, and an introduction to classical statistics. The book strikes a balance between simplicity in exposition and sophistication in analytical reasoning. Some of the more mathematically rigorous analysis is explained intuitively in the main text, and then developed in detail (at the level of advanced calculus) in the numerous solved theoretical problems. |
the probability companion for engineering and computer science: The Mathematical Theory of Information Jan Kåhre, 2002-06-30 The general concept of information is here, for the first time, defined mathematically by adding one single axiom to the probability theory. This Mathematical Theory of Information is explored in fourteen chapters: 1. Information can be measured in different units, in anything from bits to dollars. We will here argue that any measure is acceptable if it does not violate the Law of Diminishing Information. This law is supported by two independent arguments: one derived from the Bar-Hillel ideal receiver, the other is based on Shannon's noisy channel. The entropy in the 'classical information theory' is one of the measures conforming to the Law of Diminishing Information, but it has, however, properties such as being symmetric, which makes it unsuitable for some applications. The measure reliability is found to be a universal information measure. 2. For discrete and finite signals, the Law of Diminishing Information is defined mathematically, using probability theory and matrix algebra. 3. The Law of Diminishing Information is used as an axiom to derive essential properties of information. Byron's law: there is more information in a lie than in gibberish. Preservation: no information is lost in a reversible channel. Etc. The Mathematical Theory of Information supports colligation, i. e. the property to bind facts together making 'two plus two greater than four'. Colligation is a must when the information carries knowledge, or is a base for decisions. In such cases, reliability is always a useful information measure. Entropy does not allow colligation. |
the probability companion for engineering and computer science: Probability with R Jane M. Horgan, 2019-12-18 Provides a comprehensive introduction to probability with an emphasis on computing-related applications This self-contained new and extended edition outlines a first course in probability applied to computer-related disciplines. As in the first edition, experimentation and simulation are favoured over mathematical proofs. The freely down-loadable statistical programming language R is used throughout the text, not only as a tool for calculation and data analysis, but also to illustrate concepts of probability and to simulate distributions. The examples in Probability with R: An Introduction with Computer Science Applications, Second Edition cover a wide range of computer science applications, including: testing program performance; measuring response time and CPU time; estimating the reliability of components and systems; evaluating algorithms and queuing systems. Chapters cover: The R language; summarizing statistical data; graphical displays; the fundamentals of probability; reliability; discrete and continuous distributions; and more. This second edition includes: improved R code throughout the text, as well as new procedures, packages and interfaces; updated and additional examples, exercises and projects covering recent developments of computing; an introduction to bivariate discrete distributions together with the R functions used to handle large matrices of conditional probabilities, which are often needed in machine translation; an introduction to linear regression with particular emphasis on its application to machine learning using testing and training data; a new section on spam filtering using Bayes theorem to develop the filters; an extended range of Poisson applications such as network failures, website hits, virus attacks and accessing the cloud; use of new allocation functions in R to deal with hash table collision, server overload and the general allocation problem. The book is supplemented with a Wiley Book Companion Site featuring data and solutions to exercises within the book. Primarily addressed to students of computer science and related areas, Probability with R: An Introduction with Computer Science Applications, Second Edition is also an excellent text for students of engineering and the general sciences. Computing professionals who need to understand the relevance of probability in their areas of practice will find it useful. |
the probability companion for engineering and computer science: Probability and Statistics for Engineering and the Sciences + Enhanced Webassign Access , 2017 |
the probability companion for engineering and computer science: Companion Encyclopedia of the History and Philosophy of the Mathematical Sciences I. Grattan-Guinness, 2003 The second book of a two-volume encyclopaedia which makes the vast and varied history of mathematics available in a reasonably compact format. The book offers in-depth accounts of the principal areas of activity up to the 1930s and touches on related topics, including ethnomathematics. |
the probability companion for engineering and computer science: Probability and Random Processes for Electrical and Computer Engineers , 2006 The theory of probability has important applications for computer and electrical engineers as a tool to explain, model, analyse and design the technology they develop. Gubner presents the fundamentals of probability, then progresses to more complicated topics. Suitable for advanced undergraduates, graduates and as a reference for researchers. |
the probability companion for engineering and computer science: Computational Statistical Physics Lucas Böttcher, Hans J. Herrmann, 2021-08-26 Providing a detailed and pedagogical account of the rapidly-growing field of computational statistical physics, this book covers both the theoretical foundations of equilibrium and non-equilibrium statistical physics, and also modern, computational applications such as percolation, random walks, magnetic systems, machine learning dynamics, and spreading processes on complex networks. A detailed discussion of molecular dynamics simulations is also included, a topic of great importance in biophysics and physical chemistry. The accessible and self-contained approach adopted by the authors makes this book suitable for teaching courses at graduate level, and numerous worked examples and end of chapter problems allow students to test their progress and understanding. |
the probability companion for engineering and computer science: Statistics and Probability with Applications for Engineers and Scientists Bhisham C Gupta, Irwin Guttman, 2014-03-06 Introducing the tools of statistics and probability from the ground up An understanding of statistical tools is essential for engineers and scientists who often need to deal with data analysis over the course of their work. Statistics and Probability with Applications for Engineers and Scientists walks readers through a wide range of popular statistical techniques, explaining step-by-step how to generate, analyze, and interpret data for diverse applications in engineering and the natural sciences. Unique among books of this kind, Statistics and Probability with Applications for Engineers and Scientists covers descriptive statistics first, then goes on to discuss the fundamentals of probability theory. Along with case studies, examples, and real-world data sets, the book incorporates clear instructions on how to use the statistical packages Minitab® and Microsoft® Office Excel® to analyze various data sets. The book also features: • Detailed discussions on sampling distributions, statistical estimation of population parameters, hypothesis testing, reliability theory, statistical quality control including Phase I and Phase II control charts, and process capability indices • A clear presentation of nonparametric methods and simple and multiple linear regression methods, as well as a brief discussion on logistic regression method • Comprehensive guidance on the design of experiments, including randomized block designs, one- and two-way layout designs, Latin square designs, random effects and mixed effects models, factorial and fractional factorial designs, and response surface methodology • A companion website containing data sets for Minitab and Microsoft Office Excel, as well as JMP ® routines and results Assuming no background in probability and statistics, Statistics and Probability with Applications for Engineers and Scientists features a unique, yet tried-and-true, approach that is ideal for all undergraduate students as well as statistical practitioners who analyze and illustrate real-world data in engineering and the natural sciences. |
the probability companion for engineering and computer science: The Cambridge Companion to Literature and Science Steven Meyer, 2018-05-03 In 1959, C. P. Snow lamented the presence of what he called the 'two cultures': the apparently unbridgeable chasm of understanding and knowledge between modern literature and modern science. In recent decades, scholars have worked diligently and often with great ingenuity to interrogate claims like Snow's that represent twentieth- and twenty-first-century literature and science as radically alienated from each other. The Cambridge Companion to Literature and Science offers a roadmap to developments that have contributed to the demonstration and emergence of reciprocal connections between the two domains of inquiry. Weaving together theory and empiricism, individual chapters explore major figures - Shakespeare, Bacon, Emerson, Darwin, Henry James, William James, Whitehead, Einstein, Empson, and McClintock; major genres and modes of writing - fiction, science fiction, non-fiction prose, poetry, and dramatic works; and major theories and movements - pragmatism, critical theory, science studies, cognitive science, ecocriticism, cultural studies, affect theory, digital humanities, and expanded empiricisms. This book will be a key resource for scholars, graduate students, and undergraduate students alike. |
the probability companion for engineering and computer science: The Cambridge Companion to Petrarch Albert Russell Ascoli, Unn Falkeid, 2015-11-24 Petrarch (Francesco Petrarca, 1304–74), best known for his influential collection of Italian lyric poetry dedicated to his beloved Laura, was also a remarkable classical scholar, a deeply religious thinker and a philosopher of secular ethics. In this wide-ranging study, chapters by leading scholars view Petrarch's life through his works, from the epic Africa to the Letter to Posterity, from the Canzoniere to the vernacular epic Triumphi. Petrarch is revealed as the heir to the converging influences of classical cultural and medieval Christianity, but also to his great vernacular precursor, Dante, and his friend, collaborator and sly critic, Boccaccio. Particular attention is given to Petrach's profound influence on the Humanist movement and on the courtly cult of vernacular love poetry, while raising important questions as to the validity of the distinction between medieval and modern and what is lost in attempting to classify this elusive figure. |
the probability companion for engineering and computer science: The Cambridge Companion to Durkheim Jeffrey C. Alexander, Philip Smith, 2005-05-26 An authoritative and comprehensive collection of essays redefining the relevance of Durkheim to the human sciences in the twenty-first century. |
the probability companion for engineering and computer science: The Cambridge Companion to American Science Fiction Eric Carl Link, Gerry Canavan, 2015-01-26 This Companion explores the relationship between the ideas and themes of American science fiction and their roots in the American cultural experience. |
the probability companion for engineering and computer science: Concrete Mathematics Ronald L. Graham, Donald E. Knuth, Oren Patashnik, 1994-02-28 This book introduces the mathematics that supports advanced computer programming and the analysis of algorithms. The primary aim of its well-known authors is to provide a solid and relevant base of mathematical skills - the skills needed to solve complex problems, to evaluate horrendous sums, and to discover subtle patterns in data. It is an indispensable text and reference not only for computer scientists - the authors themselves rely heavily on it! - but for serious users of mathematics in virtually every discipline. Concrete Mathematics is a blending of CONtinuous and disCRETE mathematics. More concretely, the authors explain, it is the controlled manipulation of mathematical formulas, using a collection of techniques for solving problems. The subject matter is primarily an expansion of the Mathematical Preliminaries section in Knuth's classic Art of Computer Programming, but the style of presentation is more leisurely, and individual topics are covered more deeply. Several new topics have been added, and the most significant ideas have been traced to their historical roots. The book includes more than 500 exercises, divided into six categories. Complete answers are provided for all exercises, except research problems, making the book particularly valuable for self-study. Major topics include: Sums Recurrences Integer functions Elementary number theory Binomial coefficients Generating functions Discrete probability Asymptotic methods This second edition includes important new material about mechanical summation. In response to the widespread use of the first edition as a reference book, the bibliography and index have also been expanded, and additional nontrivial improvements can be found on almost every page. Readers will appreciate the informal style of Concrete Mathematics. Particularly enjoyable are the marginal graffiti contributed by students who have taken courses based on this material. The authors want to convey not only the importance of the techniques presented, but some of the fun in learning and using them. |
the probability companion for engineering and computer science: Probability, Random Variables, Statistics, and Random Processes Ali Grami, 2019-04-02 Probability, Random Variables, Statistics, and Random Processes: Fundamentals & Applications is a comprehensive undergraduate-level textbook. With its excellent topical coverage, the focus of this book is on the basic principles and practical applications of the fundamental concepts that are extensively used in various Engineering disciplines as well as in a variety of programs in Life and Social Sciences. The text provides students with the requisite building blocks of knowledge they require to understand and progress in their areas of interest. With a simple, clear-cut style of writing, the intuitive explanations, insightful examples, and practical applications are the hallmarks of this book. The text consists of twelve chapters divided into four parts. Part-I, Probability (Chapters 1 – 3), lays a solid groundwork for probability theory, and introduces applications in counting, gambling, reliability, and security. Part-II, Random Variables (Chapters 4 – 7), discusses in detail multiple random variables, along with a multitude of frequently-encountered probability distributions. Part-III, Statistics (Chapters 8 – 10), highlights estimation and hypothesis testing. Part-IV, Random Processes (Chapters 11 – 12), delves into the characterization and processing of random processes. Other notable features include: Most of the text assumes no knowledge of subject matter past first year calculus and linear algebra With its independent chapter structure and rich choice of topics, a variety of syllabi for different courses at the junior, senior, and graduate levels can be supported A supplemental website includes solutions to about 250 practice problems, lecture slides, and figures and tables from the text Given its engaging tone, grounded approach, methodically-paced flow, thorough coverage, and flexible structure, Probability, Random Variables, Statistics, and Random Processes: Fundamentals & Applications clearly serves as a must textbook for courses not only in Electrical Engineering, but also in Computer Engineering, Software Engineering, and Computer Science. |
the probability companion for engineering and computer science: The Cambridge Companion to St Paul James D. G. Dunn, 2003-10-16 The apostle Paul has been justifiably described as the first and greatest Christian theologian. His letters were among the earliest documents to be included in the New Testament and, as such, they shaped Christian thinking from the beginning. As a missionary, theologian and pastor Paul's own wrestling with theological and ethical questions of his day is paradigmatic for Christian theology, not least for Christianity's own identity and continuing relationship with Judaism. The Cambridge Companion to St Paul provides an important assessment of this apostle and a fresh appreciation of his continuing significance today. With eighteen chapters written by a team of leading international specialists on Paul, the Companion provides a sympathetic and critical overview of the apostle, covering his life and work, his letters and his theology. The volume will provide an invaluable starting point and helpful cross check for subsequent studies. |
the probability companion for engineering and computer science: Fundamentals of Probability Saeed Ghahramani, 2018-09-05 The 4th edition of Ghahramani's book is replete with intriguing historical notes, insightful comments, and well-selected examples/exercises that, together, capture much of the essence of probability. Along with its Companion Website, the book is suitable as a primary resource for a first course in probability. Moreover, it has sufficient material for a sequel course introducing stochastic processes and stochastic simulation. --Nawaf Bou-Rabee, Associate Professor of Mathematics, Rutgers University Camden, USA This book is an excellent primer on probability, with an incisive exposition to stochastic processes included as well. The flow of the text aids its readability, and the book is indeed a treasure trove of set and solved problems. Every sub-topic within a chapter is supplemented by a comprehensive list of exercises, accompanied frequently by self-quizzes, while each chapter ends with a useful summary and another rich collection of review problems. --Dalia Chakrabarty, Department of Mathematical Sciences, Loughborough University, UK This textbook provides a thorough and rigorous treatment of fundamental probability, including both discrete and continuous cases. The book’s ample collection of exercises gives instructors and students a great deal of practice and tools to sharpen their understanding. Because the definitions, theorems, and examples are clearly labeled and easy to find, this book is not only a great course accompaniment, but an invaluable reference. --Joshua Stangle, Assistant Professor of Mathematics, University of Wisconsin – Superior, USA This one- or two-term calculus-based basic probability text is written for majors in mathematics, physical sciences, engineering, statistics, actuarial science, business and finance, operations research, and computer science. It presents probability in a natural way: through interesting and instructive examples and exercises that motivate the theory, definitions, theorems, and methodology. This book is mathematically rigorous and, at the same time, closely matches the historical development of probability. Whenever appropriate, historical remarks are included, and the 2096 examples and exercises have been carefully designed to arouse curiosity and hence encourage students to delve into the theory with enthusiasm. New to the Fourth Edition: 538 new examples and exercises have been added, almost all of which are of applied nature in realistic contexts Self-quizzes at the end of each section and self-tests at the end of each chapter allow students to check their comprehension of the material An all-new Companion Website includes additional examples, complementary topics not covered in the previous editions, and applications for more in-depth studies, as well as a test bank and figure slides. It also includes complete solutions to all self-test and self-quiz problems Saeed Ghahramani is Professor of Mathematics and Dean of the College of Arts and Sciences at Western New England University. He received his Ph.D. from the University of California at Berkeley in Mathematics and is a recipient of teaching awards from Johns Hopkins University and Towson University. His research focuses on applied probability, stochastic processes, and queuing theory. |
the probability companion for engineering and computer science: The Cambridge Companion to Adam Smith Knud Haakonssen, 2006-03-06 Adam Smith is best known as the founder of scientific economics and as an early proponent of the modern market economy. Political economy, however, was only one part of Smith's comprehensive intellectual system. Consisting of a theory of mind and its functions in language, arts, science, and social intercourse, Smith's system was a towering contribution to the Scottish Enlightenment. His ideas on social intercourse also served as the basis for a moral theory that provided both historical and theoretical accounts of law, politics, and economics. This Companion volume provides an examination of all aspects of Smith's thought. Collectively, the essays take into account Smith's multiple contexts - Scottish, British, European, Atlantic; biographical, institutional, political, philosophical - and they draw on all of his works, including student notes from his lectures. Pluralistic in approach, the volume provides a contextualist history of Smith, as well as direct philosophical engagement with his ideas. |
the probability companion for engineering and computer science: The Cambridge Companion to Leibniz Nicholas Jolley, 1994-10-28 Gottfried Leibniz was a remarkable thinker who made fundamental contributions not only to philosophy, but also to the development of modern mathematics and science. At the centre of Leibniz's philosophy stands his metaphysics, an ambitious attempt to discover the nature of reality through the use of unaided reason. This volume provides a systematic and comprehensive account of the full range of Leibniz's thought, exploring the metaphysics in detail and showing its subtle and complex relationship to his views on logic, language, physics, and theology. Other chapters examine the intellectual context of his thought and its reception in the eighteenth century. New readers and nonspecialists will find this the most accessible and comprehensive guide to Leibniz currently available. Advanced students and specialists will find a conspectus of recent developments in the interpretation of Leibniz. |
the probability companion for engineering and computer science: Mathematics for Computer Science Eric Lehman, F. Thomson Leighton, Albert R. Meyer, 2017-06-05 This book covers elementary discrete mathematics for computer science and engineering. It emphasizes mathematical definitions and proofs as well as applicable methods. Topics include formal logic notation, proof methods; induction, well-ordering; sets, relations; elementary graph theory; integer congruences; asymptotic notation and growth of functions; permutations and combinations, counting principles; discrete probability. Further selected topics may also be covered, such as recursive definition and structural induction; state machines and invariants; recurrences; generating functions. The color images and text in this book have been converted to grayscale. |
the probability companion for engineering and computer science: Probability Concepts and Theory for Engineers Harry Schwarzlander, 2011-02-21 A thorough introduction to the fundamentals of probability theory This book offers a detailed explanation of the basic models and mathematical principles used in applying probability theory to practical problems. It gives the reader a solid foundation for formulating and solving many kinds of probability problems for deriving additional results that may be needed in order to address more challenging questions, as well as for proceeding with the study of a wide variety of more advanced topics. Great care is devoted to a clear and detailed development of the ‘conceptual model' which serves as the bridge between any real-world situation and its analysis by means of the mathematics of probability. Throughout the book, this conceptual model is not lost sight of. Random variables in one and several dimensions are treated in detail, including singular random variables, transformations, characteristic functions, and sequences. Also included are special topics not covered in many probability texts, such as fuzziness, entropy, spherically symmetric random variables, and copulas. Some special features of the book are: a unique step-by-step presentation organized into 86 topical Sections, which are grouped into six Parts over 200 diagrams augment and illustrate the text, which help speed the reader's comprehension of the material short answer review questions following each Section, with an answer table provided, strengthen the reader's detailed grasp of the material contained in the Section problems associated with each Section provide practice in applying the principles discussed, and in some cases extend the scope of that material an online separate solutions manual is available for course tutors. The various features of this textbook make it possible for engineering students to become well versed in the ‘machinery' of probability theory. They also make the book a useful resource for self-study by practicing engineers and researchers who need a more thorough grasp of particular topics. |
the probability companion for engineering and computer science: Boolean Algebra and Its Applications J. Eldon Whitesitt, 2012-05-24 Introductory treatment begins with set theory and fundamentals of Boolean algebra, proceeding to concise accounts of applications to symbolic logic, switching circuits, relay circuits, binary arithmetic, and probability theory. 1961 edition. |
the probability companion for engineering and computer science: Concentration of Measure Inequalities in Information Theory, Communications, and Coding Maxim Raginsky, Igal Sason, 2014 Concentration of Measure Inequalities in Information Theory, Communications, and Coding focuses on some of the key modern mathematical tools that are used for the derivation of concentration inequalities, on their links to information theory, and on their various applications to communications and coding. |
the probability companion for engineering and computer science: Fundamentals of Probability and Statistics for Engineers T. T. Soong, 2004-06-25 This textbook differs from others in the field in that it has been prepared very much with students and their needs in mind, having been classroom tested over many years. It is a true “learner’s book” made for students who require a deeper understanding of probability and statistics. It presents the fundamentals of the subject along with concepts of probabilistic modelling, and the process of model selection, verification and analysis. Furthermore, the inclusion of more than 100 examples and 200 exercises (carefully selected from a wide range of topics), along with a solutions manual for instructors, means that this text is of real value to students and lecturers across a range of engineering disciplines. Key features: Presents the fundamentals in probability and statistics along with relevant applications. Explains the concept of probabilistic modelling and the process of model selection, verification and analysis. Definitions and theorems are carefully stated and topics rigorously treated. Includes a chapter on regression analysis. Covers design of experiments. Demonstrates practical problem solving throughout the book with numerous examples and exercises purposely selected from a variety of engineering fields. Includes an accompanying online Solutions Manual for instructors containing complete step-by-step solutions to all problems. |
the probability companion for engineering and computer science: Introduction to Information Retrieval Christopher D. Manning, Prabhakar Raghavan, Hinrich Schütze, 2008-07-07 Class-tested and coherent, this textbook teaches classical and web information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. It gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Slides and additional exercises (with solutions for lecturers) are also available through the book's supporting website to help course instructors prepare their lectures. |
the probability companion for engineering and computer science: The Fundamentals of Political Science Research Paul M. Kellstedt, Guy D. Whitten, 2009 This textbook introduces the scientific study of politics, supplying students with the basic tools to be critical consumers and producers of scholarly research. |
the probability companion for engineering and computer science: The Cambridge Companion to Pragmatism Alan Malachowski, 2013-11-07 This book provides an insightful overview of what has made pragmatism such an attractive and exciting prospect to thinkers of different persuasions. |
the probability companion for engineering and computer science: Probability and Statistics Arak M. Mathai, Hans J. Haubold, 2017-12-18 This book offers an introduction to concepts of probability theory, probability distributions relevant in the applied sciences, as well as basics of sampling distributions, estimation and hypothesis testing. As a companion for classes for engineers and scientists, the book also covers applied topics such as model building and experiment design. Contents Random phenomena Probability Random variables Expected values Commonly used discrete distributions Commonly used density functions Joint distributions Some multivariate distributions Collection of random variables Sampling distributions Estimation Interval estimation Tests of statistical hypotheses Model building and regression Design of experiments and analysis of variance Questions and answers |
the probability companion for engineering and computer science: The Princeton Companion to Mathematics Timothy Gowers, June Barrow-Green, Imre Leader, 2010-07-18 The ultimate mathematics reference book This is a one-of-a-kind reference for anyone with a serious interest in mathematics. Edited by Timothy Gowers, a recipient of the Fields Medal, it presents nearly two hundred entries—written especially for this book by some of the world's leading mathematicians—that introduce basic mathematical tools and vocabulary; trace the development of modern mathematics; explain essential terms and concepts; examine core ideas in major areas of mathematics; describe the achievements of scores of famous mathematicians; explore the impact of mathematics on other disciplines such as biology, finance, and music—and much, much more. Unparalleled in its depth of coverage, The Princeton Companion to Mathematics surveys the most active and exciting branches of pure mathematics. Accessible in style, this is an indispensable resource for undergraduate and graduate students in mathematics as well as for researchers and scholars seeking to understand areas outside their specialties. Features nearly 200 entries, organized thematically and written by an international team of distinguished contributors Presents major ideas and branches of pure mathematics in a clear, accessible style Defines and explains important mathematical concepts, methods, theorems, and open problems Introduces the language of mathematics and the goals of mathematical research Covers number theory, algebra, analysis, geometry, logic, probability, and more Traces the history and development of modern mathematics Profiles more than ninety-five mathematicians who influenced those working today Explores the influence of mathematics on other disciplines Includes bibliographies, cross-references, and a comprehensive index Contributors include: Graham Allan, Noga Alon, George Andrews, Tom Archibald, Sir Michael Atiyah, David Aubin, Joan Bagaria, Keith Ball, June Barrow-Green, Alan Beardon, David D. Ben-Zvi, Vitaly Bergelson, Nicholas Bingham, Béla Bollobás, Henk Bos, Bodil Branner, Martin R. Bridson, John P. Burgess, Kevin Buzzard, Peter J. Cameron, Jean-Luc Chabert, Eugenia Cheng, Clifford C. Cocks, Alain Connes, Leo Corry, Wolfgang Coy, Tony Crilly, Serafina Cuomo, Mihalis Dafermos, Partha Dasgupta, Ingrid Daubechies, Joseph W. Dauben, John W. Dawson Jr., Francois de Gandt, Persi Diaconis, Jordan S. Ellenberg, Lawrence C. Evans, Florence Fasanelli, Anita Burdman Feferman, Solomon Feferman, Charles Fefferman, Della Fenster, José Ferreirós, David Fisher, Terry Gannon, A. Gardiner, Charles C. Gillispie, Oded Goldreich, Catherine Goldstein, Fernando Q. Gouvêa, Timothy Gowers, Andrew Granville, Ivor Grattan-Guinness, Jeremy Gray, Ben Green, Ian Grojnowski, Niccolò Guicciardini, Michael Harris, Ulf Hashagen, Nigel Higson, Andrew Hodges, F. E. A. Johnson, Mark Joshi, Kiran S. Kedlaya, Frank Kelly, Sergiu Klainerman, Jon Kleinberg, Israel Kleiner, Jacek Klinowski, Eberhard Knobloch, János Kollár, T. W. Körner, Michael Krivelevich, Peter D. Lax, Imre Leader, Jean-François Le Gall, W. B. R. Lickorish, Martin W. Liebeck, Jesper Lützen, Des MacHale, Alan L. Mackay, Shahn Majid, Lech Maligranda, David Marker, Jean Mawhin, Barry Mazur, Dusa McDuff, Colin McLarty, Bojan Mohar, Peter M. Neumann, Catherine Nolan, James Norris, Brian Osserman, Richard S. Palais, Marco Panza, Karen Hunger Parshall, Gabriel P. Paternain, Jeanne Peiffer, Carl Pomerance, Helmut Pulte, Bruce Reed, Michael C. Reed, Adrian Rice, Eleanor Robson, Igor Rodnianski, John Roe, Mark Ronan, Edward Sandifer, Tilman Sauer, Norbert Schappacher, Andrzej Schinzel, Erhard Scholz, Reinhard Siegmund-Schultze, Gordon Slade, David J. Spiegelhalter, Jacqueline Stedall, Arild Stubhaug, Madhu Sudan, Terence Tao, Jamie Tappenden, C. H. Taubes, Rüdiger Thiele, Burt Totaro, Lloyd N. Trefethen, Dirk van Dalen, Richard Weber, Dominic Welsh, Avi Wigderson, Herbert Wilf, David Wilkins, B. Yandell, Eric Zaslow, and Doron Zeilberger |
the probability companion for engineering and computer science: Mathematics for Electrical Engineering and Computing Mary P Attenborough, 2003-06-30 Mathematics for Electrical Engineering and Computing embraces many applications of modern mathematics, such as Boolean Algebra and Sets and Functions, and also teaches both discrete and continuous systems - particularly vital for Digital Signal Processing (DSP). In addition, as most modern engineers are required to study software, material suitable for Software Engineering - set theory, predicate and prepositional calculus, language and graph theory - is fully integrated into the book.Excessive technical detail and language are avoided, recognising that the real requirement for practising engineers is the need to understand the applications of mathematics in everyday engineering contexts. Emphasis is given to an appreciation of the fundamental concepts behind the mathematics, for problem solving and undertaking critical analysis of results, whether using a calculator or a computer.The text is backed up by numerous exercises and worked examples throughout, firmly rooted in engineering practice, ensuring that all mathematical theory introduced is directly relevant to real-world engineering. The book includes introductions to advanced topics such as Fourier analysis, vector calculus and random processes, also making this a suitable introductory text for second year undergraduates of electrical, electronic and computer engineering, undertaking engineering mathematics courses.Dr Attenborough is a former Senior Lecturer in the School of Electrical, Electronic and Information Engineering at South Bank University. She is currently Technical Director of The Webbery - Internet development company, Co. Donegal, Ireland. - Fundamental principles of mathematics introduced and applied in engineering practice, reinforced through over 300 examples directly relevant to real-world engineering |
the probability companion for engineering and computer science: Discrete Mathematics and Graph Theory K. Erciyes, 2021-01-28 This textbook can serve as a comprehensive manual of discrete mathematics and graph theory for non-Computer Science majors; as a reference and study aid for professionals and researchers who have not taken any discrete math course before. It can also be used as a reference book for a course on Discrete Mathematics in Computer Science or Mathematics curricula. The study of discrete mathematics is one of the first courses on curricula in various disciplines such as Computer Science, Mathematics and Engineering education practices. Graphs are key data structures used to represent networks, chemical structures, games etc. and are increasingly used more in various applications such as bioinformatics and the Internet. Graph theory has gone through an unprecedented growth in the last few decades both in terms of theory and implementations; hence it deserves a thorough treatment which is not adequately found in any other contemporary books on discrete mathematics, whereas about 40% of this textbook is devoted to graph theory. The text follows an algorithmic approach for discrete mathematics and graph problems where applicable, to reinforce learning and to show how to implement the concepts in real-world applications. |
the probability companion for engineering and computer science: Exploring RANDOMNESS Gregory J. Chaitin, 2012-12-06 In The Unknowable I use LISP to compare my work on incompleteness with that of G6del and Turing, and in The Limits of Mathematics I use LISP to discuss my work on incompleteness in more detail. In this book we'll use LISP to explore my theory of randomness, called algorithmic information theory (AIT). And when I say explore I mean it! This book is full of exercises for the reader, ranging from the mathematical equivalent oftrivial fin ger warm-ups for pianists, to substantial programming projects, to questions I can formulate precisely but don't know how to answer, to questions that I don't even know how to formulate precisely! I really want you to follow my example and hike offinto the wilder ness and explore AIT on your own! You can stay on the trails that I've blazed and explore the well-known part of AIT, or you can go off on your own and become a fellow researcher, a colleague of mine! One way or another, the goal of this book is to make you into a participant, not a passive observer of AlT. In other words, it's too easy to just listen to a recording of AIT, that's not the way to learn music. |
the probability companion for engineering and computer science: Companion Encyclopedia of the History and Philosophy of the Mathematical Sciences I. Grattan-Guinness, 2003 The first book of a two-volume encyclopaedia which makes the vast and varied history of mathematics available in a reasonably compact format. The book offers in-depth accounts of the principal areas of activity up to the 1930s and touches on related topics, including ethnomathematics. |
the probability companion for engineering and computer science: Sequential Monte Carlo Methods in Practice Arnaud Doucet, Nando de Freitas, Neil Gordon, 2013-03-09 Monte Carlo methods are revolutionising the on-line analysis of data in fields as diverse as financial modelling, target tracking and computer vision. These methods, appearing under the names of bootstrap filters, condensation, optimal Monte Carlo filters, particle filters and survial of the fittest, have made it possible to solve numerically many complex, non-standarard problems that were previously intractable. This book presents the first comprehensive treatment of these techniques, including convergence results and applications to tracking, guidance, automated target recognition, aircraft navigation, robot navigation, econometrics, financial modelling, neural networks,optimal control, optimal filtering, communications, reinforcement learning, signal enhancement, model averaging and selection, computer vision, semiconductor design, population biology, dynamic Bayesian networks, and time series analysis. This will be of great value to students, researchers and practicioners, who have some basic knowledge of probability. Arnaud Doucet received the Ph. D. degree from the University of Paris- XI Orsay in 1997. From 1998 to 2000, he conducted research at the Signal Processing Group of Cambridge University, UK. He is currently an assistant professor at the Department of Electrical Engineering of Melbourne University, Australia. His research interests include Bayesian statistics, dynamic models and Monte Carlo methods. Nando de Freitas obtained a Ph.D. degree in information engineering from Cambridge University in 1999. He is presently a research associate with the artificial intelligence group of the University of California at Berkeley. His main research interests are in Bayesian statistics and the application of on-line and batch Monte Carlo methods to machine learning. |
the probability companion for engineering and computer science: Academic Research Instruments, Expenditures ..., Needs ... , 1993 |
the probability companion for engineering and computer science: Mathematical Foundations for Signal Processing, Communications, and Networking Erchin Serpedin, Thomas Chen, Dinesh Rajan, 2017-12-04 Mathematical Foundations for Signal Processing, Communications, and Networking describes mathematical concepts and results important in the design, analysis, and optimization of signal processing algorithms, modern communication systems, and networks. Helping readers master key techniques and comprehend the current research literature, the book offers a comprehensive overview of methods and applications from linear algebra, numerical analysis, statistics, probability, stochastic processes, and optimization. From basic transforms to Monte Carlo simulation to linear programming, the text covers a broad range of mathematical techniques essential to understanding the concepts and results in signal processing, telecommunications, and networking. Along with discussing mathematical theory, each self-contained chapter presents examples that illustrate the use of various mathematical concepts to solve different applications. Each chapter also includes a set of homework exercises and readings for additional study. This text helps readers understand fundamental and advanced results as well as recent research trends in the interrelated fields of signal processing, telecommunications, and networking. It provides all the necessary mathematical background to prepare students for more advanced courses and train specialists working in these areas. |
the probability companion for engineering and computer science: Mathematics for Engineers and Science Labs Using Maxima Seifedine Kadry, Pauly Awad, 2019-02-21 This book is designed to be a vital companion to math textbooks covering the topics of precalculus, calculus, linear algebra, differential equations, and probability and statistics. While these existing textbooks focus mainly on solving mathematic problems using the old paper-and-pencil method, this book teaches how to solve these problems using Maxima open-source software. Maxima is a system for the manipulation of symbolic and numerical expressions, including differentiation, integration, Taylor series, Laplace transforms, ordinary differential equations, systems of linear equations, polynomials, sets, lists, vectors, and matrices. One of the benefits of using Maxima to solve mathematics problems is the immediacy with which it produces answers. Investing in learning Maxima now will pay off in the future, particularly for students and beginning professionals in mathematics, science, and engineering. The volume will help readers to apply nearly all of the Maxima skills discussed here to future courses and research. |
the probability companion for engineering and computer science: Biology And Computation: A Physicist's Choice Hanoch Gutfreund, Gerard Toulouse, 1994-03-04 This book provides a comprehensive review of the works in the rapidly evolving field of neural networks and brain studies. Its purpose is two-fold: to help physicists entering this field to get a broader view of the context of the domain, and to help scientists of other disciplines to reach a better understanding of the physicists' contributions within a context of perspectives they can relate to.Included in the volume are 68 carefully selected, high quality reprints to provide the volume with both breadth and depth. It is organized into 5 sections and 22 chapters, both the sections and chapters being preceded by introductory comments by the editors. |
the probability companion for engineering and computer science: Site Reliability Engineering Niall Richard Murphy, Betsy Beyer, Chris Jones, Jennifer Petoff, 2016-03-23 The overwhelming majority of a software systemâ??s lifespan is spent in use, not in design or implementation. So, why does conventional wisdom insist that software engineers focus primarily on the design and development of large-scale computing systems? In this collection of essays and articles, key members of Googleâ??s Site Reliability Team explain how and why their commitment to the entire lifecycle has enabled the company to successfully build, deploy, monitor, and maintain some of the largest software systems in the world. Youâ??ll learn the principles and practices that enable Google engineers to make systems more scalable, reliable, and efficientâ??lessons directly applicable to your organization. This book is divided into four sections: Introductionâ??Learn what site reliability engineering is and why it differs from conventional IT industry practices Principlesâ??Examine the patterns, behaviors, and areas of concern that influence the work of a site reliability engineer (SRE) Practicesâ??Understand the theory and practice of an SREâ??s day-to-day work: building and operating large distributed computing systems Managementâ??Explore Google's best practices for training, communication, and meetings that your organization can use |
Probability - Wikipedia
Probability is a branch of mathematics and statistics concerning events and numerical descriptions of how likely they are to occur. The probability of an event is a number between 0 and 1; the …
Probability - Math is Fun
How likely something is to happen. Many events can't be predicted with total certainty. The best we can say is how likely they are to happen, using the idea of probability. When a coin is tossed, …
Probability - Formula, Calculating, Find, Theorems, Examples
Probability is all about how likely is an event to happen. For a random experiment with sample space S, the probability of happening of an event A is calculated by the probability formula n (A)/n (S).
7.5: Basic Concepts of Probability - Mathematics LibreTexts
Define probability including impossible and certain events. Calculate basic theoretical probabilities. Calculate basic empirical probabilities. Distinguish among theoretical, empirical, and subjective …
Probability Definition in Math - BYJU'S
Probability is a measure of the likelihood of an event to occur. Many events cannot be predicted with total certainty. We can predict only the chance of an event to occur i.e., how likely they are …
How To Calculate Probability - Math Steps, Examples & Questions
Free how to calculate probability math topic guide, including step-by-step examples, free practice questions, teaching tips and more!
What is Probability? Definition, Types, Formula, & Examples
Apr 7, 2025 · Probability is defined as the measure of how likely an event is to happen, usually expressed as a value between zero and one. A Probability of zero indicates that the event is …
Probability in Maths - GeeksforGeeks
May 16, 2025 · In this section, you will explore the fundamental concepts of probability, key formulas, conditional probability, and Bayes' Theorem. By the end, you'll have a clear …
Probability | Brilliant Math & Science Wiki
A probability is a number that represents the likelihood of an uncertain event. Probabilities are always between 0 and 1, inclusive. The larger the probability, the more likely the event is to …
Probability - Definition, Formula, Types, Terms, Solved Problems
Jan 15, 2021 · Probability is defined as the possibility of an event to occur. The formula for Probability is given as the ratio of the number of favorable events to the total number of possible …
Probability - Wikipedia
Probability is a branch of mathematics and statistics concerning events and numerical descriptions of how likely they are to occur. The probability of …
Probability - Math is Fun
How likely something is to happen. Many events can't be predicted with total certainty. The best we can say is how likely they are to happen, using …
Probability - Formula, Calculating, Find, Theorems, E…
Probability is all about how likely is an event to happen. For a random experiment with sample space S, the probability of happening of an event …
7.5: Basic Concepts of Probability - Mathematics Lib…
Define probability including impossible and certain events. Calculate basic theoretical probabilities. Calculate basic empirical probabilities. Distinguish …
Probability Definition in Math - BYJU'S
Probability is a measure of the likelihood of an event to occur. Many events cannot be predicted with total certainty. We can predict only the …