Now available in paperback, this celebrated book has been prepared with readers' needs in mind, remaining a systematic guide to a large part of the modern theory of Probability, whilst retaining its vitality. The authors' aim is to present the subject of Brownian motion not as a dry part of mathematical analysis, but to convey its real meaning and fascination. The opening, heuristic chapter does just this, and it is followed by a comprehensive and self-contained account of the foundations of theory of stochastic processes. Chapter 3 is a lively and readable account of the theory of Markov processes. Together with its companion volume, this book helps equip graduate students for research into a subject of great intrinsic interest and wide application in physics, biology, engineering, finance and computer science.

After every crisis economists and policy analysts ask: can better models help prevent or ameliorate such situations? This book provides an answer. Yes, quantitative models can help if we remember that they are rough approximations to a vastly more complex reality. Models can help if we include realistic but simple representations of uncertainty among our models, and if we retain the pre-eminence of human judgment over the churning of our computers. Info-gap theory is a new method for modeling and managing severe uncertainty. The core of the book presents detailed examples of info-gap analysis of decisions in monetary policy, financial economics, environmental economics for pollution control and climate change, estimation and forecasting. This book is essential reading for economic policy analysts and researchers.

新的科学突破将在于物质与信息边缘的地方。对于麦克斯韦妖这个假想的科学怪物的研究证实在朝向这个方向努力。逻辑计算能够不产生熵吗？计算上的不可逆和物理上的不可逆究竟有什么关系？信息和物质究竟以怎样的方式纠缠在一起？这本书是一个论文集，对物理与信息科学的边界进行探讨。

Retaining the general organization and style of its predecessor, this new edition continues to serve as a comprehensive guide to modern and classical methods of statistical computing and computational statistics. Approaching the topic in three major parts--optimization, integration, and smoothing--the book includes an overview section in each chapter introduction and step-by-step implementation summaries to accompany the explanations of key methods; expanded coverage of Monte Carlo sampling and MCMC; a chapter on Alternative Viewpoints; a related Web site; new exercises; and more.

The fourth edition of this standard textbook of modern graph theory has been carefully revised, updated, and substantially extended. Covering all its major recent developments it can be used both as a reliable textbook for an introductory course and as a graduate text: on each topic it covers all the basic material in full detail, and adds one or two deeper results (again with detailed proofs) to illustrate the more advanced methods of that field. electronic edition: diestel-graph-theory.com From the reviews of the first two editions (1997, 2000): "This outstanding book cannot be substituted with any other book on the present textbook market. It has every chance of becoming the standard textbook for graph theory." Acta Scientiarum Mathematiciarum "The book has received a very enthusiastic reception, which it amply deserves. A masterly elucidation of modern graph theory." Bulletin of the Institute of Combinatorics and its Applications "A highlight of the book is what is by far the best account in print of the Seymour-Robertson theory of graph minors." Mathematika "...like listening to someone explain mathematics." Bulletin of the AMS

The classic book on statistical graphics, charts, tables. Theory and practice in the design of data graphics, 250 illustrations of the best (and a few of the worst) statistical graphics, with detailed analysis of how to display data for precise, effective, quick analysis. Design of the high-resolution displays, small multiples. Editing and improving graphics. The data-ink ratio. Time-series, relational graphics, data maps, multivariate designs. Detection of graphical deception: design variation vs. data variation. Sources of deception. Aesthetics and data graphical displays.
This is the second edition of The Visual Display of Quantitative Information. Recently published, this new edition provides excellent color reproductions of the many graphics of William Playfair, adds color to other images, and includes all the changes and corrections accumulated during 17 printings of the first edition.
This book celebrates escapes from the flatlands of both paper and computer screen, showing superb displays of high-dimensional complex data. The most design-oriented of Edward Tufte's books, Envisioning Information shows maps, charts, scientific presentations, diagrams, computer interfaces, statistical graphics and tables, stereo photographs, guidebooks, courtroom exhibits, timetables, use of color, a pop-up, and many other wonderful displays of information. The book provides practical advice about how to explain complex material by visual means, with extraordinary examples to illustrate the fundamental principles of information displays. Topics include escaping flatland, color and information, micro/macro designs, layering and separation, small multiples, and narratives. Winner of 17 awards for design and content. 400 illustrations with exquisite 6- to 12-color printing throughout. Highest quality design and production.
Visual Explanations: Images and Quantities, Evidence and Narrative is about pictures of verbs, the representation of mechanism and motion, process and dynamics, causes and effects, explanation and narrative. Practical applications and examples include statistical graphics, charts for making important decisions in engineering and medicine, technical manuals, diagrams, design of computer interfaces and websites and on-line manuals, animations and scientific visualizations, techniques for talks, and design strategies for enhancing the rate of information transfer in print, presentations, and computer screens. The use of visual evidence in deciding to launch the space shuttle Challenger is discussed in careful detail. Video snapshots show redesigns of a supercomputer animation of a thunderstorm. The book is designed and printed to the highest standards, with luscious color throughout and four built-in flaps for showing motion and before/after effects.

This new and updated edition deals with all aspects of Monte Carlo simulation of complex physical systems encountered in condensed-matter physics, statistical mechanics, and related fields. After briefly recalling essential background in statistical mechanics and probability theory, it gives a succinct overview of simple sampling methods. The concepts behind the simulation algorithms are explained comprehensively, as are the techniques for efficient evaluation of system configurations generated by simulation. It contains many applications, examples, and exercises to help the reader and provides many new references to more specialized literature. This edition includes a brief overview of other methods of computer simulation and an outlook for the use of Monte Carlo simulations in disciplines beyond physics. This is an excellent guide for graduate students and researchers who use computer simulations in their research. It can be used as a textbook for graduate courses on computer simulations in physics and related disciplines.

Comparing and contrasting the reality of subjectivity in the work of history's great scientists and the modern Bayesian approach to statistical analysis Scientists and researchers are taught to analyze their data from an objective point of view, allowing the data to speak for themselves rather than assigning them meaning based on expectations or opinions. But scientists have never behaved fully objectively. Throughout history, some of our greatest scientific minds have relied on intuition, hunches, and personal beliefs to make sense of empirical data-and these subjective influences have often aided in humanity's greatest scientific achievements. The authors argue that subjectivity has not only played a significant role in the advancement of science, but that science will advance more rapidly if the modern methods of Bayesian statistical analysis replace some of the classical twentieth-century methods that have traditionally been taught. To accomplish this goal, the authors examine the lives and work of history's great scientists and show that even the most successful have sometimes misrepresented findings or been influenced by their own preconceived notions of religion, metaphysics, and the occult, or the personal beliefs of their mentors. Contrary to popular belief, our greatest scientific thinkers approached their data with a combination of subjectivity and empiricism, and thus informally achieved what is more formally accomplished by the modern Bayesian approach to data analysis. Yet we are still taught that science is purely objective. This innovative book dispels that myth using historical accounts and biographical sketches of more than a dozen great scientists, including Aristotle, Galileo Galilei, Johannes Kepler, William Harvey, Sir Isaac Newton, Antoine Levoisier, Alexander von Humboldt, Michael Faraday, Charles Darwin, Louis Pasteur, Gregor Mendel, Sigmund Freud, Marie Curie, Robert Millikan, Albert Einstein, Sir Cyril Burt, and Margaret Mead. Also included is a detailed treatment of the modern Bayesian approach to data analysis. Up-to-date references to the Bayesian theoretical and applied literature, as well as reference lists of the primary sources of the principal works of all the scientists discussed, round out this comprehensive treatment of the subject. Readers will benefit from this cogent and enlightening view of the history of subjectivity in science and the authors' alternative vision of how the Bayesian approach should be used to further the cause of science and learning well into the twenty-first century.

Uncertainty is a fundamental and unavoidable feature of daily life; in order to deal with uncertaintly intelligently, we need to be able to represent it and reason about it. In this book, Joseph Halpern examines formal ways of representing uncertainty and considers various logics for reasoning about it. While the ideas presented are formalized in terms of definitions and theorems, the emphasis is on the philosophy of representing and reasoning about uncertainty; the material is accessible and relevant to researchers and students in many fields, including computer science, artificial intelligence, economics (particularly game theory), mathematics, philosophy, and statistics.Halpern begins by surveying possible formal systems for representing uncertainty, including probability measures, possibility measures, and plausibility measures. He considers the updating of beliefs based on changing information and the relation to Bayes' theorem; this leads to a discussion of qualitative, quantitative, and plausibilistic Bayesian networks. He considers not only the uncertainty of a single agent but also uncertainty in a multi-agent framework. Halpern then considers the formal logical systems for reasoning about uncertainty. He discusses knowledge and belief; default reasoning and the semantics of default; reasoning about counterfactuals, and combining probability and counterfactuals; belief revision; first-order modal logic; and statistics and beliefs. He includes a series of exercises at the end of each chapter.

This book emphasizes the creative aspects of algorithm design by examining steps used in the process of algorithms development. The heart of this creative process lies in an analogy between proving mathematical theorems by induction and designing combinatorial algorithms. The book contains hundreds of problems and examples. It is designed to enhance the reader's problem-solving abilities and understanding of the principles behind algorithm design.

This book offers robust solutions for everyday programming tasks, providing all the necessary information to
understand and use common programming techniques. It includes implementations and real-world examples of
each data structure in the text and full source code on the accompanying website
(http://examples.oreilly.com/masteralgoc/). Intended for anyone with a basic understanding of the C language.
Preface
When I first thought about writing this book, I immediately thought of O'Reilly & Associates to publish it. They were the first publisher
I contacted, and the one I most wanted to work with because of their tradition of books covering "just the facts." This approach is not
what one normally thinks of in connection with books on data structures and algorithms. When one studies data structures and
algorithms, normally there is a fair amount of time spent on proving their correctness rigorously. Consequently, many books on this
subject have an academic feel about them, and real details such as implementation and application are left to be resolved
elsewhere. This book covers how and why certain data structures and algorithms work, real applications that use them (including
many examples), and their implementation. Mathematical rigor appears only to the extent necessary in explanations.
Naturally, I was very happy that O'Reilly & Associates saw value in a book that covered this aspect of the subject. This preface
contains some of the reasons I think you will find this book valuable as well. It also covers certain aspects of the code in the book,
defines a few conventions, and gratefully acknowledges the people who played a part in the book's creation.
Bookmarks
Main Page
Table of content
Copyright
Preface
Organization
Key Features
About the Code
Conventions
How to Contact Us
Acknowledgments
Part I: Preliminaries
Chapter 1. Introduction
1.1 An Introduction to Data Structures
1.2 An Introduction to Algorithms
1.3 A Bit About Software Engineering
1.4 How to Use This Book
Chapter 2. Pointer Manipulation
2.1 Pointer Fundamentals
2.2 Storage Allocation
2.3 Aggregates and Pointer Arithmetic
2.4 Pointers as Parameters to Functions
2.5 Generic Pointers and Casts
2.6 Function Pointers
2.7 Questions and Answers
2.8 Related Topics
Chapter 3. Recursion
3.1 Basic Recursion
3.2 Tail Recursion
3.3 Questions and Answers
3.4 Related Topics
Chapter 4. Analysis of Algorithms
4.1 Worst-Case Analysis
4.2 O-Notation
4.3 Computational Complexity
4.4 Analysis Example: Insertion Sort
4.5 Questions and Answers
4.6 Related Topics
Part II: Data Structures
Chapter 5. Linked Lists
5.1 Description of Linked Lists
5.2 Interface for Linked Lists
5.3 Implementation and Analysis of Linked Lists
5.4 Linked List Example: Frame Management
5.5 Description of Doubly-Linked Lists
5.6 Interface for Doubly-Linked Lists
5.7 Implementation and Analysis of Doubly Linked Lists
5.8 Description of Circular Lists
5.9 Interface for Circular Lists
5.10 Implementation and Analysis of Circular Lists
5.11 Circular List Example: Second-Chance Page Replacement
5.12 Questions and Answers
5.13 Related Topics
Chapter 6. Stacks and Queues
6.1 Description of Stacks
6.2 Interface for Stacks
6.3 Implementation and Analysis of Stacks
6.4 Description of Queues
6.5 Interface for Queues
6.6 Implementation and Analysis of Queues
6.7 Queue Example: Event Handling
6.8 Questions and Answers
6.9 Related Topics
Chapter 7. Sets
7.1 Description of Sets
7.2 Interface for Sets
7.3 Implementation and Analysis of Sets
7.4 Set Example: Set Covering
7.5 Questions and Answers
7.6 Related Topics
Chapter 8. Hash Tables
8.1 Description of Chained Hash Tables
8.2 Interface for Chained Hash Tables
8.3 Implementation and Analysis of Chained Hash Tables
8.4 Chained Hash Table Example: Symbol Tables
8.5 Description of Open-Addressed Hash Tables
8.6 Interface for Open-Addressed Hash Tables
8.7 Implementation and Analysisof Open Addressed Hash Tables
8.8 Questions and Answers
8.9 Related Topics
Chapter 9. Trees
9.1 Description of Binary Trees
9.2 Interface for Binary Trees
9.3 Implementation and Analysis of Binary Trees
9.4 Binary Tree Example: Expression Processing
9.5 Description of Binary Search Trees
9.6 Interface for Binary Search Trees
9.7 Implementation and Analysis of Binary Search Trees
9.8 Questions and Answers
9.9 Related Topics
Chapter 10. Heaps and Priority Queues
10.1 Description of Heaps
10.2 Interface for Heaps
10.3 Implementation and Analysis of Heaps
10.4 Description of Priority Queues
10.5 Interface for Priority Queues
10.6 Implementation and Analysis of Priority Queues
10.7 Priority Queue Example: Parcel Sorting
10.8 Questions and Answers
10.9 Related Topics
Chapter 11. Graphs
11.1 Description of Graphs
11.2 Interface for Graphs
11.3 Implementation and Analysis of Graphs
11.4 Graph Example: Counting Network Hops
11.5 Graph Example: Topological Sorting
11.6 Questions and Answers
11.7 Related Topics
Part III: Algorithms
Chapter 12. Sorting and Searching
12.1 Description of Insertion Sort
12.2 Interface for Insertion Sort
12.3 Implementation and Analysis of Insertion Sort
12.4 Description of Quicksort
12.5 Interface for Quicksort
12.6 Implementation and Analysis of Quicksort
12.7 Quicksort Example: Directory Listings
12.8 Description of Merge Sort
12.9 Interface for Merge Sort
12.10 Implementation and Analysis of Merge Sort
12.11 Description of Counting Sort
12.12 Interface for Counting Sort
12.13 Implementation and Analysis of Counting Sort
12.14 Description of Radix Sort
12.15 Interface for Radix Sort
12.16 Implementation and Analysis of Radix Sort
12.17 Description of Binary Search
12.18 Interface for Binary Search
12.19 Implementation and Analysis of Binary Search
12.20 Binary Search Example: Spell Checking
12.21 Questions and Answers
12.22 Related Topics
Chapter 13. Numerical Methods
13.1 Description of Polynomial Interpolation
13.2 Interface for Polynomial Interpolation
13.3 Implementation and Analysis of Polynomial Interpolation
13.4 Description of Least-Squares Estimation
13.5 Interface for Least-Squares Estimation
13.6 Implementation and Analysis of Least-Squares Estimation
13.7 Description of the Solution of Equations
13.8 Interface for the Solution of Equations
13.9 Implementation and Analysis of the Solution of Equations
13.10 Questions and Answers
13.11 Related Topics
Chapter 14. Data Compression
14.1 Description of Bit Operations
14.2 Interface for Bit Operations
14.3 Implementation and Analysis of Bit Operations
14.4 Description of Huffman Coding
14.5 Interface for Huffman Coding
14.6 Implementation and Analysis of Huffman Coding
14.7 Huffman Coding Example: Optimized Networking
14.8 Description of LZ77
14.9 Interface for LZ77
14.10 Implementation and Analysis of LZ77
14.11 Questions and Answers
14.12 Related Topics
Chapter 15. Data Encryption
15.1 Description of DES
15.2 Interface for DES
15.3 Implementation and Analysis of DES
15.4 DES Example: Block Cipher Modes
15.5 Description of RSA
15.6 Interface for RSA
15.7 Implementation and Analysis of RSA
15.8 Questions and Answers
15.9 Related Topics
Chapter 16. Graph Algorithms
16.1 Description of Minimum Spanning Trees
16.2 Interface for Minimum Spanning Trees
16.3 Implementation and Analysis of Minimum Spanning Trees
16.4 Description of Shortest Paths
16.5 Interface for Shortest Paths
16.6 Implementation and Analysis of Shortest Paths
16.7 Shortest Paths Example: Routing Tables
16.8 Description of the Traveling-Salesman Problem
16.9 Interface for the Traveling-Salesman Problem
16.10 Implementation and Analysis of the Traveling-Salesman Problem
16.11 Questions and Answers
16.12 Related Topics
Chapter 17. Geometric Algorithms
17.1 Description of Testing Whether Line Segments Intersect
17.2 Interface for Testing Whether Line Segments Intersect
17.3 Implementation and Analysis of Testing Whether Line Segments Intersect
17.4 Description of Convex Hulls
17.5 Interface for Convex Hulls
17.6 Implementation and Analysis of Convex Hulls
17.7 Description of Arc Length on Spherical Surfaces
17.8 Interface for Arc Length on Spherical Surfaces
17.9 Implementation and Analysis of Arc Length on Spherical Surfaces
17.10 Arc Length Example: Approximating Distances on Earth
17.11 Questions and Answers
17.12 Related Topics
Colophon
index

Robert Sedgewick has thoroughly rewritten and substantially expanded and updated his popular work to provide current and comprehensive coverage of important algorithms and data structures. Christopher Van Wyk and Sedgewick have developed new C++ implementations that both express the methods in a concise and direct manner, and also provide programmers with the practical means to test them on real applications. Many new algorithms are presented, and the explanations of each algorithm are much more detailed than in previous editions. A new text design and detailed, innovative figures, with accompanying commentary, greatly enhance the presentation. The third edition retains the successful blend of theory and practice that has made Sedgewick's work an invaluable resource for more than 250,000 programmers! This particular book, Parts 1n4, represents the essential first half of Sedgewick's complete work. It provides extensive coverage of fundamental data structures and algorithms for sorting, searching, and related applications. Although the substance of the book applies to programming in any language, the implementations by Van Wyk and Sedgewick also exploit the natural match between C++ classes and ADT implementations. Highlights * Expanded coverage of arrays, linked lists, strings, trees, and other basic data structures * Greater emphasis on abstract data types (ADTs), modular programming, object-oriented programming, and C++ classes than in previous editions * Over 100 algorithms for sorting, selection, priority queue ADT implementations, and symbol table ADT (searching) implementations * New implementations of binomial queues, multiway radix sorting, randomized BSTs, splay trees, skip lists, multiway tries, B trees, extendible hashing, and much more * Increased quantitative information about the algorithms, giving you a basis for comparing them * Over 1000 new exercises to help you learn the properties of algorithms Whether you are learning the algorithms for the first time or wish to have up-to-date reference material that incorporates new programming styles with classic and new algorithms, you will find a wealth of useful information in this book.

This well-accepted introduction to computational geometry is a textbook for high-level undergraduate and low-level graduate courses. The focus is on algorithms and hence the book is well suited for students in computer science and engineering. Motivation is provided from the application areas: all solutions and techniques from computational geometry are related to particular applications in robotics, graphics, CAD/CAM, and geographic information systems. For students this motivation will be especially welcome. Modern insights in computational geometry are used to provide solutions that are both efficient and easy to understand and implement. All the basic techniques and topics from computational geometry, as well as several more advanced topics, are covered. The book is largely self-contained and can be used for self-study by anyone with a basic background in algorithms. In this third edition, besides revisions to the second edition, new sections discussing Voronoi diagrams of line segments, farthest-point Voronoi diagrams, and realistic input models have been added.

Peter Seibel interviews 15 of the most interesting computer programmers alive today in Coders at Work, offering a companion volume to Apress's highly acclaimed best-seller Founders at Work by Jessica Livingston. As the words "at work" suggest, Peter Seibel focuses on how his interviewees tackle the day-to-day work of programming, while revealing much more, like how they became great programmers, how they recognize programming talent in others, and what kinds of problems they find most interesting. Hundreds of people have suggested names of programmers to interview on the Coders at Work web site: www.codersatwork.com. The complete list was 284 names. Having digested everyone's feedback, we selected 15 folks who've been kind enough to agree to be interviewed: * Frances Allen: Pioneer in optimizing compilers, first woman to win the Turing Award (2006) and first female IBM fellow * Joe Armstrong: Inventor of Erlang * Joshua Bloch: Author of the Java collections framework, now at Google * Bernie Cosell: One of the main software guys behind the original ARPANET IMPs and a master debugger * Douglas Crockford: JSON founder, JavaScript architect at Yahoo! * L. Peter Deutsch: Author of Ghostscript, implementer of Smalltalk-80 at Xerox PARC and Lisp 1.5 on PDP-1 * Brendan Eich: Inventor of JavaScript, CTO of the Mozilla Corporation * Brad Fitzpatrick: Writer of LiveJournal, OpenID, memcached, and Perlbal * Dan Ingalls: Smalltalk implementor and designer * Simon Peyton Jones: Coinventor of Haskell and lead designer of Glasgow Haskell Compiler * Donald Knuth: Author of The Art of Computer Programming and creator of TeX * Peter Norvig: Director of Research at Google and author of the standard text on AI * Guy Steele: Coinventor of Scheme and part of the Common Lisp Gang of Five, currently working on Fortress * Ken Thompson: Inventor of UNIX * Jamie Zawinski: Author of XEmacs and early Netscape/Mozilla hacker What you'll learnHow the best programmers in the world do their jobs! Who this book is for Programmers interested in the point of view of leaders in the field. Programmers looking for approaches that work for some of these outstanding programmers. Table of Contents * Jamie Zawinski * Brad Fitzpatrick * Douglas Crockford * Brendan Eich * Joshua Bloch * Joe Armstrong * Simon Peyton Jones * Peter Norvig * Guy Steele * Dan Ingalls * L Peter Deutsch * Ken Thompson * Fran Allen * Bernie Cosell * Donald Knuth