Extends a model for automatically changing a sequential program containing FORTRAN-like do loops, introduced in vol. 1 of the series, to an equivalent parallel form consisting of do loops and assignment statements. Details the dependence between statements of the program caused by program variables that are elements of arrays. Includes exercises. For advanced undergraduates and graduate students as well as professionals writers of restructuring compilers, with background in programming languages, calculus, and graph theory, and familiarity with vol. 1 of the series, The Foundations. Knowledge of linear programming is helpful but not required. Annotation copyrighted by Book News, Inc., Portland, OR
This book is on dependence concepts and general methods for dependence testing. Here, dependence means data dependence and the tests are compile-time tests. We felt the time was ripe to create a solid theory of the subject, to provide the research community with a uniform conceptual framework in which things fit together nicely. How successful we have been in meeting these goals, of course, remains to be seen. We do not try to include all the minute details that are known, nor do we deal with clever tricks that all good programmers would want to use. We do try to convince the reader that there is a mathematical basis consisting of theories of bounds of linear functions and linear diophantine equations, that levels and direction vectors are concepts that arise rather natu rally, that different dependence tests are really special cases of some general tests, and so on. Some mathematical maturity is needed for a good understand ing of the book: mainly calculus and linear algebra. We have cov ered diophantine equations rather thoroughly and given a descrip of some matrix theory ideas that are not very widely known. tion A reader familiar with linear programming would quickly recog nize several concepts. We have learned a great deal from the works of M. Wolfe, and K. Kennedy and R. Allen. Wolfe's Ph. D. thesis at the University of Illinois and Kennedy & Allen's paper on vectorization of Fortran programs are still very useful sources on this subject.
Mathematical models are used to simulate complex real-world phenomena in many areas of science and technology. Large complex models typically require inputs whose values are not known with certainty. Uncertainty analysis aims to quantify the overall uncertainty within a model, in order to support problem owners in model-based decision-making. In recent years there has been an explosion of interest in uncertainty analysis. Uncertainty and dependence elicitation, dependence modelling, model inference, efficient sampling, screening and sensitivity analysis, and probabilistic inversion are among the active research areas. This text provides both the mathematical foundations and practical applications in this rapidly expanding area, including: An up-to-date, comprehensive overview of the foundations and applications of uncertainty analysis. All the key topics, including uncertainty elicitation, dependence modelling, sensitivity analysis and probabilistic inversion. Numerous worked examples and applications. Workbook problems, enabling use for teaching. Software support for the examples, using UNICORN - a Windows-based uncertainty modelling package developed by the authors. A website featuring a version of the UNICORN software tailored specifically for the book, as well as computer programs and data sets to support the examples. Uncertainty Analysis with High Dimensional Dependence Modelling offers a comprehensive exploration of a new emerging field. It will prove an invaluable text for researches, practitioners and graduate students in areas ranging from statistics and engineering to reliability and environmetrics.
This book constitutes the refereed proceedings of the 13th International Symposium on Static Analysis, SAS 2006. The book presents 23 revised full papers together with the abstracts of 3 invited talks. The papers address all aspects of static analysis including program and systems verification, shape analysis and logic, termination analysis, bug detection, compiler optimization, software maintenance, security and safety, abstract interpretation and algorithms, abstract domain and data structures and more.
1. Introduction : Dependence modeling / D. Kurowicka -- 2. Multivariate copulae / M. Fischer -- 3. Vines arise / R.M. Cooke, H. Joe and K. Aas -- 4. Sampling count variables with specified Pearson correlation : A comparison between a naive and a C-vine sampling approach / V. Erhardt and C. Czado -- 5. Micro correlations and tail dependence / R.M. Cooke, C. Kousky and H. Joe -- 6. The Copula information criterion and Its implications for the maximum pseudo-likelihood estimator / S. Gronneberg -- 7. Dependence comparisons of vine copulae with four or more variables / H. Joe -- 8. Tail dependence in vine copulae / H. Joe -- 9. Counting vines / O. Morales-Napoles -- 10. Regular vines : Generation algorithm and number of equivalence classes / H. Joe, R.M. Cooke and D. Kurowicka -- 11. Optimal truncation of vines / D. Kurowicka -- 12. Bayesian inference for D-vines : Estimation and model selection / C. Czado and A. Min -- 13. Analysis of Australian electricity loads using joint Bayesian inference of D-vines with autoregressive margins / C. Czado, F. Gartner and A. Min -- 14. Non-parametric Bayesian belief nets versus vines / A. Hanea -- 15. Modeling dependence between financial returns using pair-copula constructions / K. Aas and D. Berg -- 16. Dynamic D-vine model / A. Heinen and A. Valdesogo -- 17. Summary and future directions / D. Kurowicka
Computer professionals who need to understand advanced techniques for designing efficient compilers will need this book. It provides complete coverage of advanced issues in the design of compilers, with a major emphasis on creating highly optimizing scalar compilers. It includes interviews and printed documentation from designers and implementors of real-world compilation systems.
This collection of papers addresses context-dependence and methods for dealing with it. The book also records comments to the papers and the authors' replies to the comments. In this way, the contributions themselves are contextually dependent. It represents an inquiry into the activities on the semantics side of the pragmatics boundary.
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
The three-volume set, LNCS 2667, LNCS 2668, and LNCS 2669, constitutes the refereed proceedings of the International Conference on Computational Science and Its Applications, ICCSA 2003, held in Montreal, Canada, in May 2003. The three volumes present more than 300 papers and span the whole range of computational science from foundational issues in computer science and mathematics to advanced applications in virtually all sciences making use of computational techniques. The proceedings give a unique account of recent results in computational science.
This book constitutes the refereed proceedings of the 24th International Static Analysis Symposium, SAS 2017, held in New York, NY, USA, in August/September 2017. The 22 papers presented in this volume were carefully reviewed and selected from 50 submissions. The papers cover various aspects of the presentation of theoretical, practical, and applicational advances in area of static analysis that is recognized as a fundamental tool for program verification, bug detection, compiler organization, program understanding, and software maintenance.
