نام کتاب | Advances in Grid and Pervasive Computing: 4th International Conference, GPC 2009, Geneva, Switzerland, May 4-8, 2009, Proceedings |

ISBN | 9783642016707 |

نويسنده | Nabil Abdennadher:Dana Petcu |

ناشر | Springer |

سال انتشار | 2009 |

تعداد صفحات | 496 |

اندازه فايل | 14.2 |

فرمت کتاب | |

لينک دانلود | Advances in Grid and Pervasive Computing: 4th International Conference, GPC 2009, Geneva, Switzerland, May 4-8, 2009, Proceedings |

This book constitutes the refereed proceedings of the 4th International Conference on Grid and Pervasive Computing, GPC 2009, held in Geneva, Switzerland, in May 2009. The 42 revised full papers presented were carefully reviewed and selected from 112 submissions. The papers are organized in topical sections on grid economy, grid security, grid applications, middleware, scheduling, load balancing, pervasive computing, sensor networks, peer-to peer as well as fault tolerance.

This book constitutes the refereed proceedings of the 4th International Conference on Grid and Pervasive Computing, GPC 2009, held in Geneva, Switzerland, in May 2009.

The 42 revised full papers presented were carefully reviewed and selected from 112 submissions. The papers are organized in topical sections on grid economy, grid security, grid applications, middleware, scheduling, load balancing, pervasive computing, sensor networks, peer-to peer as well as fault tolerance.

نام کتاب | Mechanics of Biological Systems and Materials, Volume 2: Proceedings of the 2011 Annual Conference on Experimental and Applied Mechanics |

ISBN | 9781461402190 |

نويسنده | Tom Proulx |

ناشر | Springer |

سال انتشار | 2011 |

تعداد صفحات | 221 |

اندازه فايل | 13.8 |

فرمت کتاب | |

لينک دانلود | Mechanics of Biological Systems and Materials, Volume 2: Proceedings of the 2011 Annual Conference on Experimental and Applied Mechanics |

Mechanics of Biological Systems and Materials represents one of eight volumes of technical papers presented at the Society for Experimental Mechanics Annual Conference & Exposition on Experimental and Applied Mechanics, held at Uncasville, Connecticut, June 13-16, 2011. The full set of proceedings also includes volumes on Dynamic Behavior of Materials, Mechanics of Time-Dependent Materials and Processes in Conventional and Multifunctional Materials, MEMS and Nanotechnology; Optical Measurements, Modeling and, Metrology; Experimental and Applied Mechanics, Thermomechanics and Infra-Red Imaging, and Engineering Applications of Residual Stress.

نام کتاب | Computer and Information Sciences: Proceedings of the 25th International Symposium on Computer and Information Sciences |

ISBN | 9789048197941 |

نويسنده | Erol Gelenbe:Ricardo Lent:Georgia Sakellari:Ahmet Sacan:Hakki Toroslu:Adnan Yazici |

ناشر | Springer |

سال انتشار | 2010 |

تعداد صفحات | 440 |

اندازه فايل | 14.5 |

فرمت کتاب | |

لينک دانلود | Computer and Information Sciences: Proceedings of the 25th International Symposium on Computer and Information Sciences |

Computer and Information Sciences is a unique and comprehensive review of advanced technology and research in the field of Information Technology. It provides an up to date snapshot of research in Europe and the Far East (Hong Kong, Japan and China) in the most active areas of information technology, including Computer Vision, Data Engineering, Web Engineering, Internet Technologies, Bio-Informatics and System Performance Evaluation Methodologies.

نام کتاب | Granular Computing and Intelligent Systems: Design With Information Granules of Higher Order and Higher Type |

ISBN | 9783642198199 |

نويسنده | Witold Pedrycz:Shyi-Ming Chen |

ناشر | Springer |

سال انتشار | 2011 |

تعداد صفحات | 306 |

اندازه فايل | 4.1 |

فرمت کتاب | |

لينک دانلود | Granular Computing and Intelligent Systems: Design With Information Granules of Higher Order and Higher Type |

Information granules are fundamental conceptual entities facilitating perception of complex phenomena and contributing to the enhancement of human centricity in intelligent systems. The formal frameworks of information granules and information granulation comprise fuzzy sets, interval analysis, probability, rough sets, and shadowed sets, to name only a few representatives. Among current developments of Granular Computing, interesting options concern information granules of higher order and of higher type. The higher order information granularity is concerned with an effective formation of information granules over the space being originally constructed by information granules of lower order. This construct is directly associated with the concept of hierarchy of systems composed of successive processing layers characterized by the increasing levels of abstraction. This idea of layered, hierarchical realization of models of complex systems has gained a significant level of visibility in fuzzy modeling with the well-established concept of hierarchical fuzzy models where one strives to achieve a sound tradeoff between accuracy and a level of detail captured by the model and its level of interpretability. Higher type information granules emerge when the information granules themselves cannot be fully characterized in a purely numerical fashion but instead it becomes convenient to exploit their realization in the form of other types of information granules such as type-2 fuzzy sets, interval-valued fuzzy sets, or probabilistic fuzzy sets. Higher order and higher type of information granules constitute the focus of the studies on Granular Computing presented in this study. The book elaborates on sound methodologies of Granular Computing, algorithmic pursuits and an array of diverse applications and case studies in environmental studies, option price forecasting, and power engineering.

Information granules are fundamental conceptual entities facilitating perception of complex phenomena and contributing to the enhancement of human centricity in intelligent systems. The formal frameworks of information granules and information granulation comprise fuzzy sets, interval analysis, probability, rough sets, and shadowed sets, to name only a few representatives. Among current developments of Granular Computing, interesting options concern information granules of higher order and of higher type. The higher order information granularity is concerned with an effective formation of information granules over the space being originally constructed by information granules of lower order. This construct is directly associated with the concept of hierarchy of systems composed of successive processing layers characterized by the increasing levels of abstraction. This idea of layered, hierarchical realization of models of complex systems has gained a significant level of visibility in fuzzy modeling with the well-established concept of hierarchical fuzzy models where one strives to achieve a sound tradeoff between accuracy and a level of detail captured by the model and its level of interpretability. Higher type information granules emerge when the information granules themselves cannot be fully characterized in a purely numerical fashion but instead it becomes convenient to exploit their realization in the form of other types of information granules such as type-2 fuzzy sets, interval-valued fuzzy sets, or probabilistic fuzzy sets. Higher order and higher type of information granules constitute the focus of the studies on Granular Computing presented in this study. The book elaborates on sound methodologies of Granular Computing, algorithmic pursuits and an array of diverse applications and case studies in environmental studies, option price forecasting, and power engineering.

نام کتاب | Evolutionary Statistical Procedures: An Evolutionary Computation Approach to Statistical Procedures Designs and Applications |

ISBN | 9783642162183 |

نويسنده | Roberto Baragona:Francesco Battaglia:Irene Poli |

ناشر | Springer |

سال انتشار | 2011 |

تعداد صفحات | 289 |

اندازه فايل | 3.4 |

فرمت کتاب | |

لينک دانلود | Evolutionary Statistical Procedures: An Evolutionary Computation Approach to Statistical Procedures Designs and Applications |

This proposed text appears to be a good introduction to evolutionary computation for use in applied statistics research. The authors draw from a vast base of knowledge about the current literature in both the design of evolutionary algorithms and statistical techniques. Modern statistical research is on the threshold of solving increasingly complex problems in high dimensions, and the generalization of its methodology to parameters whose estimators do not follow mathematically simple distributions is underway. Many of these challenges involve optimizing functions for which analytic solutions are infeasible. Evolutionary algorithms represent a powerful and easily understood means of approximating the optimum value in a variety of settings. The proposed text seeks to guide readers through the crucial issues of optimization problems in statistical settings and the implementation of tailored methods (including both stand-alone evolutionary algorithms and hybrid crosses of these procedures with standard statistical algorithms like Metropolis-Hastings) in a variety of applications. This book would serve as an excellent reference work for statistical researchers at an advanced graduate level or beyond, particularly those with a strong background in computer science.

نام کتاب | Tools for Statistical Inference: Methods for the Exploration of Posterior Distributions and Likelihood Functions |

ISBN | 9780387946887 |

نويسنده | Martin A. Tanner |

ناشر | Springer |

سال انتشار | 1996 |

تعداد صفحات | 213 |

اندازه فايل | 19.1 |

فرمت کتاب | |

لينک دانلود | Tools for Statistical Inference: Methods for the Exploration of Posterior Distributions and Likelihood Functions |

A unified introduction to a variety of computational algorithms for likelihood and Bayesian inference. This third edition expands the discussion of many of the techniques presented, and includes additional examples as well as exercise sets at the end of each chapter.

نام کتاب | Hybrid Random Fields: A Scalable Approach to Structure and Parameter Learning in Probabilistic Graphical Models |

ISBN | 3642203078 |

نويسنده | Antonino Freno:Edmondo Trentin |

ناشر | Springer |

سال انتشار | 2011 |

تعداد صفحات | 217 |

اندازه فايل | 2.2 |

فرمت کتاب | |

لينک دانلود | Hybrid Random Fields: A Scalable Approach to Structure and Parameter Learning in Probabilistic Graphical Models |

This book presents an exciting new synthesis of directed and undirected, discrete and continuous graphical models. Combining elements of Bayesian networks and Markov random fields, the newly introduced hybrid random fields are an interesting approach to get the best of both these worlds, with an added promise of modularity and scalability. The authors have written an enjoyable book---rigorous in the treatment of the mathematical background, but also enlivened by interesting and original historical and philosophical perspectives.

-- Manfred Jaeger, Aalborg Universitet

The book not only marks an effective direction of investigation with significant experimental advances, but it is also---and perhaps primarily---a guide for the reader through an original trip in the space of probabilistic modeling. While digesting the book, one is enriched with a very open view of the field, with full of stimulating connections. [...] Everyone specifically interested in Bayesian networks and Markov random fields should not miss it.

-- Marco Gori, Università degli Studi di Siena

Graphical models are sometimes regarded---incorrectly---as an impractical approach to machine learning, assuming that they only work well for low-dimensional applications and discrete-valued domains. While guiding the reader through the major achievements of this research area in a technically detailed yet accessible way, the book is concerned with the presentation and thorough (mathematical and experimental) investigation of a novel paradigm for probabilistic graphical modeling, the hybrid random field. This model subsumes and extends both Bayesian networks and Markov random fields. Moreover, it comes with well-defined learning algorithms, both for discrete and continuous-valued domains, which fit the needs of real-world applications involving large-scale, high-dimensional data.

This book presents an exciting new synthesis of directed and undirected, discrete and continuous graphical models. Combining elements of Bayesian networks and Markov random fields, the newly introduced hybrid random fields are an interesting approach to get the best of both these worlds, with an added promise of modularity and scalability. The authors have written an enjoyable book---rigorous in the treatment of the mathematical background, but also enlivened by interesting and original historical and philosophical perspectives.

-- Manfred Jaeger, Aalborg Universitet

The book not only marks an effective direction of investigation with significant experimental advances, but it is also---and perhaps primarily---a guide for the reader through an original trip in the space of probabilistic modeling. While digesting the book, one is enriched with a very open view of the field, with full of stimulating connections. [...] Everyone specifically interested in Bayesian networks and Markov random fields should not miss it.

-- Marco Gori, Università degli Studi di Siena

Graphical models are sometimes regarded---incorrectly---as an impractical approach to machine learning, assuming that they only work well for low-dimensional applications and discrete-valued domains. While guiding the reader through the major achievements of this research area in a technically detailed yet accessible way, the book is concerned with the presentation and thorough (mathematical and experimental) investigation of a novel paradigm for probabilistic graphical modeling, the hybrid random field. This model subsumes and extends both Bayesian networks and Markov random fields. Moreover, it comes with well-defined learning algorithms, both for discrete and continuous-valued domains, which fit the needs of real-world applications involving large-scale, high-dimensional data.

نام کتاب | Dynamical Inverse Problems: Theory and Application |

ISBN | 3709106958 |

نويسنده | Graham M. L. Gladwell:Antonino Morassi |

ناشر | Springer |

سال انتشار | 2011 |

تعداد صفحات | 229 |

اندازه فايل | 3.2 |

فرمت کتاب | |

لينک دانلود | Dynamical Inverse Problems: Theory and Application |

The papers in this volume present an overview of the general aspects and practical applications of dynamic inverse methods, through the interaction of several topics, ranging from classical and advanced inverse problems in vibration, isospectral systems, dynamic methods for structural identification, active vibration control and damage detection, imaging shear stiffness in biological tissues, wave propagation, to computational and experimental aspects relevant for engineering problems.

نام کتاب | Applied Probability |

ISBN | 0387004254 |

نويسنده | Kenneth Lange |

ناشر | Springer |

سال انتشار | 2003 |

تعداد صفحات | 390 |

اندازه فايل | 2.1 |

فرمت کتاب | |

لينک دانلود | {accesstext mode="level" level="registered"} برای مشاهده لینک دانلود لطفاً وارد سایت شوید||Applied Probability {/accesstext} |

This textbook on applied probability is intended for graduate students in applied mathematics, biostatistics, computational biology, computer science, physics, and statistics. It presupposes knowledge of multivariate calculus, linear algebra, ordinary differential equations, and elementary probability theory. Given these prerequisites, Applied Probability presents a unique blend of theory and applications, with special emphasis on mathematical modeling, computational techniques, and examples from the biological sciences. Chapter 1 reviews elementary probability and provides a brief survey of relevant results from measure theory. Chapter 2 is an extended essay on calculating expectations. Chapter 3 deals with probabilistic applications of convexity, inequalities, and optimization theory. Chapters 4 and 5 touch on combinatorics and combinatorial optimization. Chapters 6 through 11 present core material on stochastic processes. If supplemented with appropriate sections from Chapters 1 and 2, there is sufficient material here for a traditional semester-long course in stochastic processes covering the basics of Poisson processes, Markov chains, branching processes, martingales, and diffusion processes. Finally, Chapters 12 and 13 develop the Chen-Stein method of Poisson approximation and connections between probability and number theory. Kenneth Lange is Professor of Biomathematics and Human Genetics and Chair of the Department of Human Genetics at the UCLA School of Medicine. He has held appointments at the University of New Hampshire, MIT, Harvard, and the University of Michigan. While at the University of Michigan, he was the Pharmacia & Upjohn Foundation Professor of Biostatistics. His research interests include human genetics, population modeling, biomedical imaging, computational statistics, and applied stochastic processes. Springer-Verlag published his books Numerical Analysis for Statisticians and Mathematical and Statistical Methods for Genetic Analysis Second Edition, in 1999 and 2002, respectively.

From the reviews:

"This book was written to convey both the 'beauty and utility of probability.' The author achieves this by providing a mixture of theory and application. To include so many different and interesting applications of probability, the author chose to minimize the number of proofs. Instead, he provides examples and written explanations…The unique aspect of this text is that the author presents applications not normally included in probability texts. There are new and very useful applications of probability that would usually be found in journal articles or in a number of different textbooks." Technometrics, May 2004

"…Pretty applications in computer science and genetics strengthen the overall message of this book, namely to give applied probability the attention it deserves." Short Book Reviews of the International Statistical Institute, April 2004

"In his Preface, he worries that the pursuit of mathematical rigor discourages students of science, particularly of biology, from learning the powerful tools that modern probability theory puts at their disposal. This book is an attempt to remedy that…Professor Lange’s book is certainly a pleasure to read, and it is written in a clear style using standard probabilistic notation." Journal of the American Statistical Association, June 2004

"This book is intended for graduate students in applied mathematics, biostatistics, computational biology, computer science, physics and statistics. … The book presents a mixture of theory and applications, with emphasis on mathematical modeling and computational techniques. It contains a number of examples from the biological sciences. … All chapters have exercises and hints are provided for some of the difficult problems. … Students should find this book stimulating, refreshing and highly useful." (R. Subramanian, Mathematical Reviews, 2004a)

"The book would be a delight to use. … Lange has produced an enjoyable, highly readable book … . I found much of the material of interest … . All of the chapters come with exercises some of them challenging. … a course based on this material would be a joy." (Jeffrey J. Hunter, New Zealand Mathematical Society Newsletter, Issue 89, December, 2003)

"Lange makes every possible effort to keep a delicate balance between theory and applications. He presents the material in a clear and informative manner that will appeal to all interested readers … . Lange offers numerous illustrative examples from biological sciences and challenging chapter end problems. This interesting and useful book presents clearly the applicability of probabilistic tools to solve problems in different disciplines. Summing Up: Recommended. Researchers and graduate students in genetics, mathematics, physics, biostatistics, computer science, and statistics." (D.V. Chopra, CHOICE, September, 2003)

"The author tries to offer to the scientific community at large an introduction to some of the most important aspects of applied probability. From the table of contents, it is clear that the author has chosen a very personal approach … . this choice illustrates the beauty, utility and relevance of probabilistic thinking in a variety of scientific areas. In particular, pretty applications in computer science and genetics strengthen the overall message of this book, namely to give applied probability the attention it deserves." (J.L. Teugels, Short Book Reviews, Vol. 24 (1), 2004)

Applied Probability presents a unique blend of theory and applications, with special emphasis on mathematical modeling, computational techniques, and examples from the biological sciences. It can serve as a textbook for graduate students in applied mathematics, biostatistics, computational biology, computer science, physics, and statistics. Readers should have a working knowledge of multivariate calculus, linear algebra, ordinary differential equations, and elementary probability theory. Chapter 1 reviews elementary probability and provides a brief survey of relevant results from measure theory. Chapter 2 is an extended essay on calculating expectations. Chapter 3 deals with probabilistic applications of convexity, inequalities, and optimization theory. Chapters 4 and 5 touch on combinatorics and combinatorial optimization. Chapters 6 through 11 present core material on stochastic processes. If supplemented with appropriate sections from Chapters 1 and 2, there is sufficient material for a traditional semester-long course in stochastic processes covering the basics of Poisson processes, Markov chains, branching processes, martingales, and diffusion processes. The second edition adds two new chapters on asymptotic and numerical methods and an appendix that separates some of the more delicate mathematical theory from the steady flow of examples in the main text. Besides the two new chapters, the second edition includes a more extensive list of exercises, many additions to the exposition of combinatorics, new material on rates of convergence to equilibrium in reversible Markov chains, a discussion of basic reproduction numbers in population modeling, and better coverage of Brownian motion. Because many chapters are nearly self-contained, mathematical scientists from a variety of backgrounds will find Applied Probability useful as a reference. Kenneth Lange is the Rosenfeld Professor of Computational Genetics in the Departments of Biomathematics and Human Genetics at the UCLA School of Medicine and the Chair of the Department of Human Genetics. His research interests include human genetics, population modeling, biomedical imaging, computational statistics, high-dimensional optimization, and applied stochastic processes. Springer previously published his books Mathematical and Statistical Methods for Genetic Analysis, 2nd ed., Numerical Analysis for Statisticians, 2nd ed., and Optimization. He has written over 200 research papers and produced with his UCLA colleague Eric Sobel the computer program Mendel, widely used in statistical genetics.

نام کتاب | A Primer on Scientific Programming With Python |

ISBN | 3642302920 |

نويسنده | Hans Petter Langtangen |

ناشر | Springer |

سال انتشار | 2012 |

تعداد صفحات | 832 |

اندازه فايل | 6.2 |

فرمت کتاب | |

لينک دانلود | {accesstext mode="level" level="registered"} برای مشاهده لینک دانلود لطفاً وارد سایت شوید||A Primer on Scientific Programming With Python {/accesstext} |

The book serves as a first introduction to computer programming of scientific applications, using the high-level Python language. The exposition is example- and problem-oriented, where the applications are taken from mathematics, numerical calculus, statistics, physics, biology, and finance. The book teaches "Matlab-style" and procedural programming as well as object-oriented programming. High school mathematics is a required background, and it is advantageous to study classical and numerical one-variable calculus in parallel with reading this book. Besides learning how to program computers, the reader will also learn how to solve mathematical problems, arising in various branches of science and engineering, with the aid of numerical methods and programming. By blending programming, mathematics and scientific applications, the book lays a solid foundation for practicing computational science.