## Probability Models for Computer Science

**Author**: Sheldon M. Ross

**Publisher:**Taylor & Francis US

**ISBN:**9780125980517

**Category:**Computers

**Page:**288

**View:**6434

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The role of probability in computer science has been growing for years and, in lieu of a tailored textbook, many courses have employed a variety of similar, but not entirely applicable, alternatives. To meet the needs of the computer science graduate student (and the advanced undergraduate), best-selling author Sheldon Ross has developed the premier probability text for aspiring computer scientists involved in computer simulation and modeling. The math is precise and easily understood. As with his other texts, Sheldon Ross presents very clear explanations of concepts and covers those probability models that are most in demand by, and applicable to, computer science and related majors and practitioners. Many interesting examples and exercises have been chosen to illuminate the techniques presented Examples relating to bin packing, sorting algorithms, the find algorithm, random graphs, self-organising list problems, the maximum weighted independent set problem, hashing, probabilistic verification, max SAT problem, queuing networks, distributed workload models, and many othersMany interesting examples and exercises have been chosen to illuminate the techniques presented

## Introduction to Probability Models

**Author**: Sheldon M. Ross

**Publisher:**Academic Press

**ISBN:**0125980620

**Category:**Mathematics

**Page:**782

**View:**6527

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Rosss classic bestseller has been used extensively by professionals and as the primary text for a first undergraduate course in applied probability. With the addition of several new sections relating to actuaries, this text is highly recommended by the Society of Actuaries.

## Introduction to Probability Models, ISE

**Author**: Sheldon M. Ross

**Publisher:**Academic Press

**ISBN:**0080920179

**Category:**Mathematics

**Page:**800

**View:**9944

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Ross's classic bestseller, Introduction to Probability Models, has been used extensively by professionals and as the primary text for a first undergraduate course in applied probability. It provides an introduction to elementary probability theory and stochastic processes, and shows how probability theory can be applied to the study of phenomena in fields such as engineering, computer science, management science, the physical and social sciences, and operations research. With the addition of several new sections relating to actuaries, this text is highly recommended by the Society of Actuaries. A new section (3.7) on COMPOUND RANDOM VARIABLES, that can be used to establish a recursive formula for computing probability mass functions for a variety of common compounding distributions. A new section (4.11) on HIDDDEN MARKOV CHAINS, including the forward and backward approaches for computing the joint probability mass function of the signals, as well as the Viterbi algorithm for determining the most likely sequence of states. Simplified Approach for Analyzing Nonhomogeneous Poisson processes Additional results on queues relating to the (a) conditional distribution of the number found by an M/M/1 arrival who spends a time t in the system; (b) inspection paradox for M/M/1 queues (c) M/G/1 queue with server breakdown Many new examples and exercises.

## Probability and Statistics for Computer Science

**Author**: David Forsyth

**Publisher:**Springer

**ISBN:**3319644106

**Category:**Computers

**Page:**367

**View:**5788

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This textbook is aimed at computer science undergraduates late in sophomore or early in junior year, supplying a comprehensive background in qualitative and quantitative data analysis, probability, random variables, and statistical methods, including machine learning. With careful treatment of topics that fill the curricular needs for the course, Probability and Statistics for Computer Science features: • A treatment of random variables and expectations dealing primarily with the discrete case. • A practical treatment of simulation, showing how many interesting probabilities and expectations can be extracted, with particular emphasis on Markov chains. • A clear but crisp account of simple point inference strategies (maximum likelihood; Bayesian inference) in simple contexts. This is extended to cover some confidence intervals, samples and populations for random sampling with replacement, and the simplest hypothesis testing. • A chapter dealing with classification, explaining why it’s useful; how to train SVM classifiers with stochastic gradient descent; and how to use implementations of more advanced methods such as random forests and nearest neighbors. • A chapter dealing with regression, explaining how to set up, use and understand linear regression and nearest neighbors regression in practical problems. • A chapter dealing with principal components analysis, developing intuition carefully, and including numerous practical examples. There is a brief description of multivariate scaling via principal coordinate analysis. • A chapter dealing with clustering via agglomerative methods and k-means, showing how to build vector quantized features for complex signals. Illustrated throughout, each main chapter includes many worked examples and other pedagogical elements such as boxed Procedures, Definitions, Useful Facts, and Remember This (short tips). Problems and Programming Exercises are at the end of each chapter, with a summary of what the reader should know. Instructor resources include a full set of model solutions for all problems, and an Instructor's Manual with accompanying presentation slides.

## Rich probabilistic models for genomic data

**Author**: Eran Segal,Stanford University. Computer Science Dept

**Publisher:**N.A

**ISBN:**N.A

**Category:**

**Page:**272

**View:**2139

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## Probability and Statistics for Computer Scientists

**Author**: Michael Baron

**Publisher:**CRC Press

**ISBN:**1420011421

**Category:**Mathematics

**Page:**432

**View:**8815

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In modern computer science, software engineering, and other fields, the need arises to make decisions under uncertainty. Presenting probability and statistical methods, simulation techniques, and modeling tools, Probability and Statistics for Computer Scientists helps students solve problems and make optimal decisions in uncertain conditions, select stochastic models, compute probabilities and forecasts, and evaluate performance of computer systems and networks. After introducing probability and distributions, this easy-to-follow textbook provides two course options. The first approach is a probability-oriented course that begins with stochastic processes, Markov chains, and queuing theory, followed by computer simulations and Monte Carlo methods. The second approach is a more standard, statistics-emphasized course that focuses on statistical inference, estimation, hypothesis testing, and regression. Assuming one or two semesters of college calculus, the book is illustrated throughout with numerous examples, exercises, figures, and tables that stress direct applications in computer science and software engineering. It also provides MATLAB® codes and demonstrations written in simple commands that can be directly translated into other computer languages. By the end of this course, advanced undergraduate and beginning graduate students should be able to read a word problem or a corporate report, realize the uncertainty involved in the described situation, select a suitable probability model, estimate and test its parameters based on real data, compute probabilities of interesting events and other vital characteristics, and make appropriate conclusions and forecasts.

## Probability and Statistics for Computer Science

**Author**: James L. Johnson

**Publisher:**John Wiley & Sons

**ISBN:**1118165969

**Category:**Mathematics

**Page:**760

**View:**3635

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Comprehensive and thorough development of both probability andstatistics for serious computer scientists; goal-oriented: "topresent the mathematical analysis underlying probabilityresults" Special emphases on simulation and discrete decision theory Mathematically-rich, but self-contained text, at a gentlepace Review of calculus and linear algebra in an appendix Mathematical interludes (in each chapter) which examinemathematical techniques in the context of probabilistic orstatistical importance Numerous section exercises, summaries, historical notes, andFurther Readings for reinforcement of content

## Linear Algebra and Probability for Computer Science Applications

**Author**: Ernest Davis

**Publisher:**CRC Press

**ISBN:**1466501553

**Category:**Mathematics

**Page:**431

**View:**4538

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Based on the author’s course at NYU, Linear Algebra and Probability for Computer Science Applications gives an introduction to two mathematical fields that are fundamental in many areas of computer science. The course and the text are addressed to students with a very weak mathematical background. Most of the chapters discuss relevant MATLAB® functions and features and give sample assignments in MATLAB; the author’s website provides the MATLAB code from the book. After an introductory chapter on MATLAB, the text is divided into two sections. The section on linear algebra gives an introduction to the theory of vectors, matrices, and linear transformations over the reals. It includes an extensive discussion on Gaussian elimination, geometric applications, and change of basis. It also introduces the issues of numerical stability and round-off error, the discrete Fourier transform, and singular value decomposition. The section on probability presents an introduction to the basic theory of probability and numerical random variables; later chapters discuss Markov models, Monte Carlo methods, information theory, and basic statistical techniques. The focus throughout is on topics and examples that are particularly relevant to computer science applications; for example, there is an extensive discussion on the use of hidden Markov models for tagging text and a discussion of the Zipf (inverse power law) distribution. Examples and Programming Assignments The examples and programming assignments focus on computer science applications. The applications covered are drawn from a range of computer science areas, including computer graphics, computer vision, robotics, natural language processing, web search, machine learning, statistical analysis, game playing, graph theory, scientific computing, decision theory, coding, cryptography, network analysis, data compression, and signal processing. Homework Problems Comprehensive problem sections include traditional calculation exercises, thought problems such as proofs, and programming assignments that involve creating MATLAB functions.

## Probability Models for DNA Sequence Evolution

**Author**: Richard Durrett

**Publisher:**Springer Science & Business Media

**ISBN:**9780387781693

**Category:**Mathematics

**Page:**431

**View:**2236

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"What underlying forces are responsible for the observed patterns of variability, given a collection of DNA sequences?" In approaching this question a number of probability models are introduced and anyalyzed.Throughout the book, the theory is developed in close connection with data from more than 60 experimental studies that illustrate the use of these results.

## Probability Models and Statistical Analyses for Ranking Data

**Author**: Michael A. Fligner,Joseph S. Verducci

**Publisher:**Springer Science & Business Media

**ISBN:**1461227380

**Category:**Mathematics

**Page:**306

**View:**628

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In June of 1990, a conference was held on Probablity Models and Statisti cal Analyses for Ranking Data, under the joint auspices of the American Mathematical Society, the Institute for Mathematical Statistics, and the Society of Industrial and Applied Mathematicians. The conference took place at the University of Massachusetts, Amherst, and was attended by 36 participants, including statisticians, mathematicians, psychologists and sociologists from the United States, Canada, Israel, Italy, and The Nether lands. There were 18 presentations on a wide variety of topics involving ranking data. This volume is a collection of 14 of these presentations, as well as 5 miscellaneous papers that were contributed by conference participants. We would like to thank Carole Kohanski, summer program coordinator for the American Mathematical Society, for her assistance in arranging the conference; M. Steigerwald for preparing the manuscripts for publication; Martin Gilchrist at Springer-Verlag for editorial advice; and Persi Diaconis for contributing the Foreword. Special thanks go to the anonymous referees for their careful readings and constructive comments. Finally, we thank the National Science Foundation for their sponsorship of the AMS-IMS-SIAM Joint Summer Programs. Contents Preface vii Conference Participants xiii Foreword xvii 1 Ranking Models with Item Covariates 1 D. E. Critchlow and M. A. Fligner 1. 1 Introduction. . . . . . . . . . . . . . . 1 1. 2 Basic Ranking Models and Their Parameters 2 1. 3 Ranking Models with Covariates 8 1. 4 Estimation 9 1. 5 Example. 11 1. 6 Discussion. 14 1. 7 Appendix . 15 1. 8 References.

## Statistik-Workshop für Programmierer

**Author**: Allen B. Downey

**Publisher:**O'Reilly Germany

**ISBN:**3868993436

**Category:**Computers

**Page:**160

**View:**9948

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Wenn Sie programmieren können, beherrschen Sie bereits Techniken, um aus Daten Wissen zu extrahieren. Diese kompakte Einführung in die Statistik zeigt Ihnen, wie Sie rechnergestützt, anstatt auf mathematischem Weg Datenanalysen mit Python durchführen können. Praktischer Programmier-Workshop statt grauer Theorie: Das Buch führt Sie anhand eines durchgängigen Fallbeispiels durch eine vollständige Datenanalyse -- von der Datensammlung über die Berechnung statistischer Kennwerte und Identifikation von Mustern bis hin zum Testen statistischer Hypothesen. Gleichzeitig werden Sie mit statistischen Verteilungen, den Regeln der Wahrscheinlichkeitsrechnung, Visualisierungsmöglichkeiten und vielen anderen Arbeitstechniken und Konzepten vertraut gemacht. Statistik-Konzepte zum Ausprobieren: Entwickeln Sie über das Schreiben und Testen von Code ein Verständnis für die Grundlagen von Wahrscheinlichkeitsrechnung und Statistik: Überprüfen Sie das Verhalten statistischer Merkmale durch Zufallsexperimente, zum Beispiel indem Sie Stichproben aus unterschiedlichen Verteilungen ziehen. Nutzen Sie Simulationen, um Konzepte zu verstehen, die auf mathematischem Weg nur schwer zugänglich sind. Lernen Sie etwas über Themen, die in Einführungen üblicherweise nicht vermittelt werden, beispielsweise über die Bayessche Schätzung. Nutzen Sie Python zur Bereinigung und Aufbereitung von Rohdaten aus nahezu beliebigen Quellen. Beantworten Sie mit den Mitteln der Inferenzstatistik Fragestellungen zu realen Daten.

## Introduction to Probability and Statistics for Engineers and Scientists

**Author**: Sheldon M. Ross

**Publisher:**Elsevier

**ISBN:**9780080470313

**Category:**Mathematics

**Page:**640

**View:**7124

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Introduction to Probability and Statistics for Engineers and Scientists, Third Edition, provides an introduction to applied probability and statistics for engineering or science majors . This updated text emphasizes the manner in which probability yields insight into statistical problems, ultimately resulting in an intuitive understanding of the statistical procedures most often used by practicing engineers and scientists. The Third Edition includes new exercises, examples, homework problems, updated statistical material, and more. New exercises and data examples include: the one-sided Chebyshev inequality for data; logistics distribution and logistic regression; estimation and testing in proofreader problems; and product form estimates of life distributions. Real data sets are incorporated in a wide variety of exercises and examples throughout the book, and the enclosed CD-ROM includes unique, easy-to-use software that automates the required computations. This book is intended primarily for undergraduates in engineering and the sciences, and would be of particular interest to students in Industrial Engineering, Operations Research, Statistics, Mathematics, Computer Science, Electrical Engineering, Civil Engineering, Chemical Engineering, and Quantitative Business. It could also be of value in a graduate introductory course in probability and statistics. New in this edition: * New exercises and data examples including: - The One-sided Chebyshev Inequality for Data - The Logistics Distribution and Logistic Regression - Estimation and Testing in proofreader problems - Product Form Estimates of Life Distributions - Observational Studies * Updated statistical material * New, contemporary applications Hallmark features: * Reflects Sheldon Ross's masterfully clear exposition * Contains numerous examples, exercises, and homework problems * Unique, easy-to-use software automates required computations * Applies probability theory to everyday statistical problems and situations * Careful development of probability, modeling, and statistical procedures leads to intuitive understanding * Instructor's Solutions Manual is available to adopters

## Introduction to Probability

*Models and Applications*

**Author**: N. Balakrishnan,Markos V. Koutras,Politis G. Konstadinos

**Publisher:**Wiley

**ISBN:**1118123344

**Category:**Mathematics

**Page:**608

**View:**2328

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An essential guide to the concepts of probability theory that puts the focus on models and applications Introduction to Probability offers an authoritative text that presents the main ideas and concepts, as well as the theoretical background, models, and applications of probability. The authors—noted experts in the field—include a review of problems where probabilistic models naturally arise, discuss the appropriate statistical methods, and explain how these models fit into the data presented. To aid in understanding, the book presents many real-world exercises and solutions that appear after each section within a chapter. A wide-range of topics are covered that include concepts of probability, univariate discrete distribution, univariate continuous distributions, bivariate discrete random variables, bivariate continuous random variables, stochastic independence-multivariate random variables, and many more. Designed as a useful guide, the text contains theory of probability, definitions, charts, examples, illustrations, problems and solutions, and a glossary. This important text: Includes classroom-tested problems and solutions to probability exercises Highlights real-world exercises designed to make clear the concepts presented Uses Matlab software to illustrate the text’s computer exercises Features applications representing worldwide situations and processes Offers a Student Solutions Manual that contains select solutions to numerous exercises found in the book Written for students majoring in statistics, engineering, operations research, computer science, physics, and mathematics, Introduction to Probability: Models and Applications is an accessible text that explores the basic concepts of probability and includes detailed information on models and applications.

## Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics

**Author**: Christine Sinoquet,Raphaël Mourad

**Publisher:**OUP Oxford

**ISBN:**0191019208

**Category:**Science

**Page:**464

**View:**5203

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Nowadays bioinformaticians and geneticists are faced with myriad high-throughput data usually presenting the characteristics of uncertainty, high dimensionality and large complexity. These data will only allow insights into this wealth of so-called 'omics' data if represented by flexible and scalable models, prior to any further analysis. At the interface between statistics and machine learning, probabilistic graphical models (PGMs) represent a powerful formalism to discover complex networks of relations. These models are also amenable to incorporating a priori biological information. Network reconstruction from gene expression data represents perhaps the most emblematic area of research where PGMs have been successfully applied. However these models have also created renewed interest in genetics in the broad sense, in particular regarding association genetics, causality discovery, prediction of outcomes, detection of copy number variations, and epigenetics. This book provides an overview of the applications of PGMs to genetics, genomics and postgenomics to meet this increased interest. A salient feature of bioinformatics, interdisciplinarity, reaches its limit when an intricate cooperation between domain specialists is requested. Currently, few people are specialists in the design of advanced methods using probabilistic graphical models for postgenomics or genetics. This book deciphers such models so that their perceived difficulty no longer hinders their use and focuses on fifteen illustrations showing the mechanisms behind the models. Probabilistic Graphical Models for Genetics, Genomics and Postgenomics covers six main themes: (1) Gene network inference (2) Causality discovery (3) Association genetics (4) Epigenetics (5) Detection of copy number variations (6) Prediction of outcomes from high-dimensional genomic data. Written by leading international experts, this is a collection of the most advanced work at the crossroads of probabilistic graphical models and genetics, genomics, and postgenomics. The self-contained chapters provide an enlightened account of the pros and cons of applying these powerful techniques.

## Discrete Probability Models and Methods

*Probability on Graphs and Trees, Markov Chains and Random Fields, Entropy and Coding*

**Author**: Pierre Brémaud

**Publisher:**Springer

**ISBN:**3319434764

**Category:**Mathematics

**Page:**559

**View:**2101

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The emphasis in this book is placed on general models (Markov chains, random fields, random graphs), universal methods (the probabilistic method, the coupling method, the Stein-Chen method, martingale methods, the method of types) and versatile tools (Chernoff's bound, Hoeffding's inequality, Holley's inequality) whose domain of application extends far beyond the present text. Although the examples treated in the book relate to the possible applications, in the communication and computing sciences, in operations research and in physics, this book is in the first instance concerned with theory. The level of the book is that of a beginning graduate course. It is self-contained, the prerequisites consisting merely of basic calculus (series) and basic linear algebra (matrices). The reader is not assumed to be trained in probability since the first chapters give in considerable detail the background necessary to understand the rest of the book.

## Applied Probability-Computer Science

*The Interface*

**Author**: Ralph L. Disney,Teunis J. Ott

**Publisher:**Birkhauser

**ISBN:**N.A

**Category:**Science

**Page:**500

**View:**441

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## Applied Probability-Computer Science: The Interface

**Author**: Ralph L. Disney,Teunis J. Ott

**Publisher:**Springer Science & Business Media

**ISBN:**1461257913

**Category:**Juvenile Nonfiction

**Page:**505

**View:**9917

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These two volumes are the Proceedings of the first special interest meeting instigated and organized by the joint Technical Section and College in Applied Probability of ORSA and THlS. This meeting, which took place January 5-7, 1981 at Florida Atlantic University in Boca Raton, Florida, had the same name as these Proceedings: Applied Probability-Computer Science, the Interface. The goal of that conference was to achieve a meeting of, and a cross fertilization between, two groups of researchers who, from different starting points, had come to work on similar problems, often developing similar methodologies and tools. One of these groups are the applied probabilists, many of whom consider their field an offspring of mathematics, and who find their motivation in many areas of application. The other is that group of computer scientists who, over the years, have found an increasing need in their work for the use of probabilistic models. The most visible area of common methodology between these two groups is networks of queues, Hhich by itself could have been the theme of an entire conference. FunctionQl areas which are, or are becoming, sources of exciting problems are computer performance analysis, data base analysis, analysis of communication protocols, data networks, and mixed voice-data telephone networks. The reader can add to this list by going through the papers in these Proceedings.

## Computer Aided Verification

*17th International Conference, CAV 2005, Edinburgh, Scotland, UK, July 6-10, 2005, Proceedings*

**Author**: Kousha Etessami

**Publisher:**Springer Science & Business Media

**ISBN:**9783540272311

**Category:**Computers

**Page:**564

**View:**9812

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This book constitutes the refereed proceedings of the 17th International Conference on Computer Aided Verification, CAV 2005, held in Edinburgh, Scotland, UK in July 2005. The 32 revised full papers presented together with 16 tool papers and 3 invited papers, as well as a report on a special tools competition were carefully reviewed and selected from 155 submissions. The papers cover all current issues in computer aided verification and model checking, ranging from foundational and methodological issues to the evaluation of major tools and systems.

## Automated Technology for Verification and Analysis

*4th International Symposium, ATVA 2006, Beijing, China, October 23-26, 2006, Proceedings*

**Author**: Susanne Graf,Wenhui Zhang

**Publisher:**Springer Science & Business Media

**ISBN:**3540472371

**Category:**Computers

**Page:**546

**View:**7561

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This book constitutes the refereed proceedings of the Third International Symposium on Automated Technology for Verification and Analysis, ATVA 2006, held in Beijing, China in October 2006. The 35 revised full papers presented together with abstracts of three keynote papers were carefully reviewed and selected from 137 submissions.