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Probabilità, Statistica e Simulazione: Programmi applicativi scritti in R (UNITEXT #131)
by Alberto Rotondi Paolo Pedroni Antonio PievatoloIl libro contiene in forma compatta il programma svolto negli insegnamenti introduttivi di Statistica e tratta alcuni argomenti indispensabili per l'attività di ricerca, come le tecniche di simulazione Monte Carlo, i metodi di inferenza statistica, di best fit e di analisi dei dati di laboratorio. Gli argomenti vengono sviluppati partendo dai fondamenti, evidenziandone gli aspetti applicativi, fino alla descrizione dettagliata di molti casi di particolare rilevanza in ambito scientifico e tecnico. Il testo è rivolto agli studenti universitari dei corsi ad indirizzo scientifico e a tutti quei ricercatori che devono risolvere problemi concreti che coinvolgono l’analisi dei dati e le tecniche di simulazione. In questa edizione, completamente rivista e corretta, sono stati aggiunti alcuni importanti argomenti sul test d’ipotesi (a cui è stato dedicato un capitolo interamente nuovo) e sul trattamento degli errori sistematici. Per la prima volta è stato adottato il software R, con una ricca libreria di programmi originali accessibile al lettore.
Probability and Random Processes for Electrical and Computer Engineers
by John A. GubnerThe theory of probability is a powerful tool that helps electrical and computer engineers to explain, model, analyze, and design the technology they develop. The text begins at the advanced undergraduate level, assuming only a modest knowledge of probability, and progresses through more complex topics mastered at graduate level. The first five chapters cover the basics of probability and both discrete and continuous random variables. The later chapters have a more specialized coverage, including random vectors, Gaussian random vectors, random processes, Markov Chains, and convergence. Describing tools and results that are used extensively in the field, this is more than a textbook; it is also a reference for researchers working in communications, signal processing, and computer network traffic analysis. With over 300 worked examples, some 800 homework problems, and sections for exam preparation, this is an essential companion for advanced undergraduate and graduate students. Further resources for this title, including solutions (for Instructors only), are available online at www. cambridge. org/9780521864701.
Probability and Statistics for Data Science: Math + R + Data (Chapman & Hall/CRC Data Science Series)
by Norman MatloffProbability and Statistics for Data Science: Math + R + Data covers "math stat"—distributions, expected value, estimation etc.—but takes the phrase "Data Science" in the title quite seriously: <P><P> * Real datasets are used extensively. * All data analysis is supported by R coding. * Includes many Data Science applications, such as PCA, mixture distributions, random graph models, Hidden Markov models, linear and logistic regression, and neural networks. * Leads the student to think critically about the "how" and "why" of statistics, and to "see the big picture." * Not "theorem/proof"-oriented, but concepts and models are stated in a mathematically precise manner. <P><P> Prerequisites are calculus, some matrix algebra, and some experience in programming. <P><P> Norman Matloff is a professor of computer science at the University of California, Davis, and was formerly a statistics professor there. He is on the editorial boards of the Journal of Statistical Software and The R Journal. His book Statistical Regression and Classification: From Linear Models to Machine Learning was the recipient of the Ziegel Award for the best book reviewed in Technometrics in 2017. He is a recipient of his university's Distinguished Teaching Award.
Probability and Statistics for Machine Learning: A Textbook
by Charu C. AggarwalThis book covers probability and statistics from the machine learning perspective. The chapters of this book belong to three categories: 1. The basics of probability and statistics: These chapters focus on the basics of probability and statistics, and cover the key principles of these topics. Chapter 1 provides an overview of the area of probability and statistics as well as its relationship to machine learning. The fundamentals of probability and statistics are covered in Chapters 2 through 5. 2. From probability to machine learning: Many machine learning applications are addressed using probabilistic models, whose parameters are then learned in a data-driven manner. Chapters 6 through 9 explore how different models from probability and statistics are applied to machine learning. Perhaps the most important tool that bridges the gap from data to probability is maximum-likelihood estimation, which is a foundational concept from the perspective of machine learning. This concept is explored repeatedly in these chapters. 3. Advanced topics: Chapter 10 is devoted to discrete-state Markov processes. It explores the application of probability and statistics to a temporal and sequential setting, although the applications extend to more complex settings such as graphical data. Chapter 11 covers a number of probabilistic inequalities and approximations. The style of writing promotes the learning of probability and statistics simultaneously with a probabilistic perspective on the modeling of machine learning applications. The book contains over 200 worked examples in order to elucidate key concepts. Exercises are included both within the text of the chapters and at the end of the chapters. The book is written for a broad audience, including graduate students, researchers, and practitioners.
Probability and Statistics with Reliability, Queuing, and Computer Science Applications
by Kishor S. TrivediAn accessible introduction to probability, stochastic processes, and statistics for computer science and engineering applications This updated and revised edition of the popular classic 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.
Probability Collectives
by Anand Jayant Kulkarni Kang Tai Ajith AbrahamThis book provides an emerging computational intelligence tool in the framework of collective intelligence for modeling and controlling distributed multi-agent systems referred to as Probability Collectives. In the modified Probability Collectives methodology a number of constraint handling techniques are incorporated, which also reduces the computational complexity and improved the convergence and efficiency. Numerous examples and real world problems are used for illustration, which may also allow the reader to gain further insight into the associated concepts.
Probability for Information Technology
by Changho SuhThis book introduces probabilistic modelling and explores its role in solving a broad spectrum of engineering problems that arise in Information Technology (IT). Divided into three parts, it begins by laying the foundation of basic probability concepts such as sample space, events, conditional probability, independence, total probability law and random variables. The second part delves into more advanced topics including random processes and key principles like Maximum A Posteriori (MAP) estimation, the law of large numbers and the central limit theorem. The last part applies these principles to various IT domains like communication, social networks, speech recognition, and machine learning, emphasizing the practical aspect of probability through real-world examples, case studies, and Python coding exercises. A notable feature of this book is its narrative style, seamlessly weaving together probability theories with both classical and contemporary IT applications. Each concept is reinforced with tightly-coupled exercise sets, and the associated fundamentals are explored mostly from first principles. Furthermore, it includes programming implementations of illustrative examples and algorithms, complemented by a brief Python tutorial. Departing from traditional organization, the book adopts a lecture-notes format, presenting interconnected themes and storylines. Primarily tailored for sophomore-level undergraduates, it also suits junior and senior-level courses. While readers benefit from mathematical maturity and programming exposure, supplementary materials and exercise problems aid understanding. Part III serves to inspire and provide insights for students and professionals alike, underscoring the pragmatic relevance of probabilistic concepts in IT.
Probability for Statistics and Machine Learning
by Anirban DasguptaThis book provides a versatile and lucid treatment of classic as well as modern probability theory, while integrating them with core topics in statistical theory and also some key tools in machine learning. It is written in an extremely accessible style, with elaborate motivating discussions and numerous worked out examples and exercises. The book has 20 chapters on a wide range of topics, 423 worked out examples, and 808 exercises. It is unique in its unification of probability and statistics, its coverage and its superb exercise sets, detailed bibliography, and in its substantive treatment of many topics of current importance. This book can be used as a text for a year long graduate course in statistics, computer science, or mathematics, for self-study, and as an invaluable research reference on probabiliity and its applications. Particularly worth mentioning are the treatments of distribution theory, asymptotics, simulation and Markov Chain Monte Carlo, Markov chains and martingales, Gaussian processes, VC theory, probability metrics, large deviations, bootstrap, the EM algorithm, confidence intervals, maximum likelihood and Bayes estimates, exponential families, kernels, and Hilbert spaces, and a self contained complete review of univariate probability.
Probability in Electrical Engineering and Computer Science: An Application-Driven Course
by Jean WalrandThis revised textbook motivates and illustrates the techniques of applied probability by applications in electrical engineering and computer science (EECS). The author presents information processing and communication systems that use algorithms based on probabilistic models and techniques, including web searches, digital links, speech recognition, GPS, route planning, recommendation systems, classification, and estimation. He then explains how these applications work and, along the way, provides the readers with the understanding of the key concepts and methods of applied probability. Python labs enable the readers to experiment and consolidate their understanding. The book includes homework, solutions, and Jupyter notebooks. This edition includes new topics such as Boosting, Multi-armed bandits, statistical tests, social networks, queuing networks, and neural networks. For ancillaries related to this book, including examples of Python demos and also Python labs used in Berkeley, please email Mary James at mary.james@springer.com. This is an open access book.
Probability, Markov Chains, Queues, and Simulation: The Mathematical Basis of Performance Modeling
by William J. StewartProbability, Markov Chains, Queues, and Simulation provides a modern and authoritative treatment of the mathematical processes that underlie performance modeling. The detailed explanations of mathematical derivations and numerous illustrative examples make this textbook readily accessible to graduate and advanced undergraduate students taking courses in which stochastic processes play a fundamental role. The textbook is relevant to a wide variety of fields, including computer science, engineering, operations research, statistics, and mathematics.The textbook looks at the fundamentals of probability theory, from the basic concepts of set-based probability, through probability distributions, to bounds, limit theorems, and the laws of large numbers. Discrete and continuous-time Markov chains are analyzed from a theoretical and computational point of view. Topics include the Chapman-Kolmogorov equations; irreducibility; the potential, fundamental, and reachability matrices; random walk problems; reversibility; renewal processes; and the numerical computation of stationary and transient distributions. The M/M/1 queue and its extensions to more general birth-death processes are analyzed in detail, as are queues with phase-type arrival and service processes. The M/G/1 and G/M/1 queues are solved using embedded Markov chains; the busy period, residual service time, and priority scheduling are treated. Open and closed queueing networks are analyzed. The final part of the book addresses the mathematical basis of simulation.Each chapter of the textbook concludes with an extensive set of exercises. An instructor's solution manual, in which all exercises are completely worked out, is also available (to professors only).Numerous examples illuminate the mathematical theoriesCarefully detailed explanations of mathematical derivations guarantee a valuable pedagogical approachEach chapter concludes with an extensive set of exercises
Probability Models
by John HaighThe purpose of this book is to provide a sound introduction to the study of real-world phenomena that possess random variation. It describes how to set up and analyse models of real-life phenomena that involve elements of chance. Motivation comes from everyday experiences of probability, such as that of a dice or cards, the idea of fairness in games of chance, and the random ways in which, say, birthdays are shared or particular events arise. Applications include branching processes, random walks, Markov chains, queues, renewal theory, and Brownian motion. This popular second edition textbook contains many worked examples and several chapters have been updated and expanded. Some mathematical knowledge is assumed. The reader should have the ability to work with unions, intersections and complements of sets; a good facility with calculus, including integration, sequences and series; and appreciation of the logical development of an argument. Probability Models is designed to aid students studying probability as part of an undergraduate course on mathematics or mathematics and statistics.
Probability, Random Variables, and Random Processes
by John J. ShynkProbability, Random Variables, and Random Processes is a comprehensive textbook on probability theory for engineers that provides a more rigorous mathematical framework than is usually encountered in undergraduate courses. It is intended for first-year graduate students who have some familiarity with probability and random variables, though not necessarily of random processes and systems that operate on random signals. It is also appropriate for advanced undergraduate students who have a strong mathematical background.The book has the following features:Several appendices include related material on integration, important inequalities and identities, frequency-domain transforms, and linear algebra. These topics have been included so that the book is relatively self-contained. One appendix contains an extensive summary of 33 random variables and their properties such as moments, characteristic functions, and entropy.Unlike most books on probability, numerous figures have been included to clarify and expand upon important points. Over 600 illustrations and MATLAB plots have been designed to reinforce the material and illustrate the various characterizations and properties of random quantities.Sufficient statistics are covered in detail, as is their connection to parameter estimation techniques. These include classical Bayesian estimation and several optimality criteria: mean-square error, mean-absolute error, maximum likelihood, method of moments, and least squares.The last four chapters provide an introduction to several topics usually studied in subsequent engineering courses: communication systems and information theory; optimal filtering (Wiener and Kalman); adaptive filtering (FIR and IIR); and antenna beamforming, channel equalization, and direction finding. This material is available electronically at the companion website.Probability, Random Variables, and Random Processes is the only textbook on probability for engineers that includes relevant background material, provides extensive summaries of key results, and extends various statistical techniques to a range of applications in signal processing.
Probability Theory: An Introduction Using R
by Shailaja R. Deshmukh Akanksha S. KashikarThis book introduces Probability Theory with R software and explains abstract concepts in a simple and easy-to-understand way by combining theory and computation. It discusses conceptual and computational examples in detail, to provide a thorough understanding of basic techniques and develop an enjoyable read for students seeking suitable material for self-study. It illustrates fundamental concepts including fields, sigma-fields, random variables and their expectations, various modes of convergence of a sequence of random variables, laws of large numbers and the central limit theorem. Computational exercises based on R software are included in each Chapter Includes a brief introduction to the basic functions of R software for beginners in R and serves as a ready reference Includes Numerical computations, simulation studies, and visualizations using R software as easy tools to explain abstract concepts Provides multiple-choice questions for practice Incorporates self-explanatory R codes in every chapter This textbook is for advanced students, professionals, and academic researchers of Statistics, Biostatistics, Economics and Mathematics.
Probably Approximately Correct: Nature's Algorithms for Learning and Prospering in a Complex World
by Leslie ValiantWe have effective theories for very few things. Gravity is one, electromagnetism another. But for most thingsOCowhether as mundane as finding a mate or as major as managing an economyOCoour theories are lousy or nonexistent. Fortunately, we donOCOt need them, any more than a fish needs a theory of water to swim; weOCOre able to muddle through. But how do we do it? In "Probably Approximately Correct," computer scientist Leslie Valiant presents a theory of the theoryless. The key is OC probably approximately correctOCO learning, ValiantOCOs model of how anything can act without needing to understand what is going on. The study of probably approximately correct algorithms reveals the shared computational nature of evolution and cognition, indicates how computers might possess authentic intelligence, and shows why hacking a problem can be far more effective than developing a theory to explain it. After all, finding a mate is a lot more satisfying than finding a theory of mating. Offering an elegant, powerful model that encompasses all of lifeOCOs complexity, "Probably Approximately Correct" will revolutionize the way we look at the universeOCOs greatest mysteries.
Probe Suppression in Conformal Phased Array
by Hema Singh Mausumi Dutta P. S. NeethuThis book considers a cylindrical phased array with microstrip patch antenna elements and half-wavelength dipole antenna elements. The effect of platform and mutual coupling effect is included in the analysis. The non-planar geometry is tackled by using Euler's transformation towards the calculation of array manifold. Results are presented for both conducting and dielectric cylinder. The optimal weights obtained are used to generate adapted pattern according to a given signal scenario. It is shown that array along with adaptive algorithm is able to cater to an arbitrary signal environment even when the platform effect and mutual coupling is taken into account. This book provides a step-by-step approach for analyzing the probe suppression in non-planar geometry. Its detailed illustrations and analysis will be a useful text for graduate and research students, scientists and engineers working in the area of phased arrays, low-observables and stealth technology.
Probing Popular Culture: On and Off the Internet
by Marshall Fishwick"When it comes to seeing depth and lateral connections in the development of popular culture, nobody exceeds Marshall Fishwick." -Canadian Psychology In Probing Popular Culture: On and Off the Internet, one of the leading authorities in American and popular culture studies presents an eye-opening examination o
Problem-based Learning: A Research Perspective on Learning Interactions
by Dorothy H. Evensen Cindy E. Hmelo Cindy E. Hmelo-SilverThis volume collects recent studies conducted within the area of medical education that investigate two of the critical components of problem-based curricula--the group meeting and self-directed learning--and demonstrates that understanding these complex phenomena is critical to the operation of this innovative curriculum. It is the editors' contention that it is these components of problem-based learning that connect the initiating "problem" with the process of effective "learning." Revealing how this occurs is the task taken on by researchers contributing to this volume. The studies include use of self-reports, interviews, observations, verbal protocols, and micro-analysis to find ways into the psychological processes and sociological contexts that constitute the world of problem-based learning.
Problem-Based Learning: A Didactic Strategy in the Teaching of System Simulation (Studies in Computational Intelligence #824)
by Lorenzo Cevallos-Torres Miguel Botto-TobarThis book describes and outlines the theoretical foundations of system simulation in teaching, and as a practical contribution to teaching-and-learning models. It presents various methodologies used in teaching, the goal being to solve real-life problems by creating simulation models and probability distributions that allow correlations to be drawn between a real model and a simulated model. Moreover, the book demonstrates the role of simulation in decision-making processes connected to teaching and learning.
Problem Solving And Program Design In C
by Jeri Hanly Elliot KoffmanProblem Solving and Program Design in C teaches readers to program with ANSI-C, a standardized, industrial-strength programming language known for its power and probability. The text uses widely accepted software engineering methods to teach readers to design cohesive, adaptable, and reusable program solution modules with ANSI-C. Through case studies and real world examples, readers are able to envision a professional career in programming.
Problem Solving for Wireless Sensor Networks
by Ana-Belén García-Hernando Juan-Manuel López-Navarro Aggeliki Prayati Luis Redondo-López José-Fernán Martínez-OrtegaProblem Solving for Wireless Sensor Networks delivers a comprehensive review of the state of the art in the most important technological issues related to Wireless Sensor Networks (WSN). It covers topics such as hardware platforms, radio technologies, software technologies (including middleware), and network and deployment aspects. This book discusses the main open issues inside each of these categories and identifies innovations considered most interesting for future research. Features: - Hardware Platforms in WSN, - Software Technologies in SWN, - Network Aspects and Deployment in WSN, - Standards and Safety Regulation for WSN, - European Projects Related to WSN, - WSN Application Scenarios at both utility and technical levels. Complete, cutting-edge and resulting from the work of many recognized researchers, Problem Solving for Wireless Sensor Networks is an invaluable reference for graduates and researchers, as well as practitioners.
Problem Solving In Operation Management
by Patricia Esperanza Balderas-Cañas Gabriel de las Nieves Sánchez-GuerreroThis volume examines problem solving and applied systems aimed at improving performance and management of organizations. The book’s eight chapters are integrated into two parts: methodologies and techniques that discuss complex dynamic analysis of the organizations, participative processes for building trend scenarios, consultancy as a systemic intervention process, processes to promote innovative goals in organizations, and analytical processes and solid mathematical representation systems. The authors also include a model to urban parks location, an analytic model to urban services location, and a system to forecast demand with fussy sets.Describes methodologies to analyze processes in complex dynamic organizations, including as participative, interventional, innovative, and analytical approaches;Clarifies a strategies for providing structure to complex organizations and applying analytical methods to decision making;Illustrates problem holistic solving strategies;Explains how to approach several problems from a holistic point of view and how analyze the subjacent processes to make decisions.
Problem Solving With C++ (Tenth Edition)
by Walter Savitch Kenrick MockNow in its 10th Edition, Problem Solving with C++ is written for the beginning programmer. The text cultivates strong problem-solving skills and programming techniques as it introduces readers to the C++ programming language. Author Walt Savitch's approach to programming emphasizes active reading through the use of well-placed examples and self-tests, while flexible coverage means the order of chapters and sections can easily be adapted without sacrificing continuity. Savitch's clear, concise style is a hallmark feature of the text and is supported by a suite of tried-and-true pedagogical tools. The 10th Edition includes ten new Programming Projects, along with new discussions and revisions.
The Problem with Native JavaScript APIs: External JavaScript Libraries Still Matter
by Nicholas C. ZakasMany features inspired by popular JavaScript libraries are now available as native JavaScript APIs in today’s powerful browsers. While that may seem convenient given all of the JavaScript you need to write, relying on these APIs will only make code maintenance more difficult in the long run.In this report, Nicholas Zakas—consultant and former front-end tech leader at Yahoo!—provides a case study to show how different browsers can develop native APIs for the same specification and still end up with different interpretations. You’ll discover how these APIs can tie your code to specific browsers, forcing you to upgrade application logic whenever new browsers and new browser versions are released.
The Problem with Software: Why Smart Engineers Write Bad Code
by Adam BarrAn industry insider explains why there is so much bad software—and why academia doesn't teach programmers what industry wants them to know. Why is software so prone to bugs? So vulnerable to viruses? Why are software products so often delayed, or even canceled? Is software development really hard, or are software developers just not that good at it? In The Problem with Software, Adam Barr examines the proliferation of bad software, explains what causes it, and offers some suggestions on how to improve the situation. For one thing, Barr points out, academia doesn't teach programmers what they actually need to know to do their jobs: how to work in a team to create code that works reliably and can be maintained by somebody other than the original authors. As the size and complexity of commercial software have grown, the gap between academic computer science and industry has widened. It's an open secret that there is little engineering in software engineering, which continues to rely not on codified scientific knowledge but on intuition and experience. Barr, who worked as a programmer for more than twenty years, describes how the industry has evolved, from the era of mainframes and Fortran to today's embrace of the cloud. He explains bugs and why software has so many of them, and why today's interconnected computers offer fertile ground for viruses and worms. The difference between good and bad software can be a single line of code, and Barr includes code to illustrate the consequences of seemingly inconsequential choices by programmers. Looking to the future, Barr writes that the best prospect for improving software engineering is the move to the cloud. When software is a service and not a product, companies will have more incentive to make it good rather than “good enough to ship."
Problems, Methods and Tools in Experimental and Behavioral Economics: Computational Methods in Experimental Economics (CMEE) 2017 Conference (Springer Proceedings in Business and Economics)
by Kesra Nermend Małgorzata ŁatuszyńskaThese proceedings highlight research on the latest trends and methods in experimental and behavioral economics. Featuring contributions presented at the 2017 Computational Methods in Experimental Economics (CMEE) conference, which was held in Lublin, Poland, it merges findings from various domains to present deep insights into topics such as game theory, decision theory, cognitive neuroscience and artificial intelligence. The fields of experimental economics and behavioral economics are rapidly evolving. Modern applications of experimental economics require the integration of know-how from disciplines including economics, computer science, psychology and neuroscience. The use of computer technology enhances researchers’ ability to generate and analyze large amounts of data, allowing them to use non-standard methods of data logging for experiments such as cognitive neuronal methods. Experiments are currently being conducted with software that, on the one hand, provides interaction with the people involved in experiments, and on the other helps to accurately record their responses. The goal of the CMEE conference and the papers presented here is to provide the scientific community with essential research on and applications of computer methods in experimental economics. Combining theories, methods and regional case studies, the book offers a valuable resource for all researchers, scholars and policymakers in the areas of experimental and behavioral economics.