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Jumat, 24 Desember 2010

[R482.Ebook] Ebook Download Exterior Analysis: Using Applications of Differential Forms, by Erdogan Suhubi

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Exterior analysis uses differential forms (a mathematical technique) to analyze curves, surfaces, and structures. Exterior Analysis is a first-of-its-kind resource that uses applications of differential forms, offering a mathematical approach to solve problems in defining a precise measurement to ensure structural integrity.

The book provides methods to study different types of equations and offers detailed explanations of fundamental theories and techniques to obtain concrete solutions to determine symmetry. It is a useful tool for structural, mechanical and electrical engineers, as well as physicists and mathematicians.

Provides a thorough explanation of how to apply differential equations to solve real-world engineering problems
Helps researchers in mathematics, science, and engineering develop skills needed to implement mathematical techniques in their research
Includes physical applications and methods used to solve practical problems to determine symmetry

Sales Rank: #747637 in Books
Published on: 2013-09-12
Original language: English
Number of items: 1
Dimensions: 9.25" h x 1.63" w x 7.52" l, 3.21 pounds
Binding: Hardcover
779 pages

Review

"The book is carefully written. It contains a rich material and covers an important part of differential geometry. Applications of the main abstract results can be found frequently. There are many examples and exercises…The book is useful for mathematicians, applied scientists and engineers."--Zentralblatt MATH, 1277.53001 "Suhubi introduces mathematicians, physicists, and engineers to the fundamental concepts and tools of exterior analysis, helps them gain competence in using these tools, and describes the advantages of the approach. He keeps the mathematics as simple as possible without sacrificing rigor. His topics include differential manifolds, tensor fields on manifolds, exterior differential forms, the integration of exterior forms, partial differential equations,…"--ProtoView.com, February 2014

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15 of 15 people found the following review helpful.
Extremely useful!
By Sergio O. Parreiras
This book is a great for anyone who wants the foundations to work with differential forms. Probably it would be very useful for graduate students or even undergraduate students who ware taking classes in differential geometry or differential topology as an **additional** reference. In particular, I'm using it to study exterior differential systems and PDE systems. Check to see if your library has the hard-copy or the electronic version because the only minus is that it's price is too expensive (very common with titles published by World Scientific). In part I guess its size and quality justifies part of the price. I love that it has lots of examples and the author does not shy away of doing calculations. Too often in math textbooks all the grinding work is left as exercises but here this is not the case. Also the book has a good selection of problems. Best book on differential forms I ever read. Also the language is more accessible to scientists from other fields than mathematics; there is no category theory jargon muddling the text. It beats Darling's book ( Differential Forms Connections R Darling fair and square.

6 of 6 people found the following review helpful.
Clean modern presentation.
By A. Ali
This is a grad level text. It's written in a more informal manner than the usual pure math text -- i.e., the usual unmotivated staccato style of definition-lemma-theorem-corollary is absent and the author tries to explain the meaning of the ideas, interspersed with calculations from time to time.But it is still an intimidating text that covers quite a bit of terrain and would probably be suitable only for grad level students. I see it as a more discursive and motivated reincarnation of Bishop and Goldberg's terse "Tensor Analysis on Manifolds."

Suhubi covers -- among other topics -- forms, integration of forms, connections, elementary Lie theory, variational calculus, and de Rham cohomology. He doesn't cover spectral sequences or characteristic classes (for the former, try chapter 3 of Bott and Tu's "Differential Forms in Algebraic Topology," and for the latter, either chapter 4 of Bott and Tu or the second half of Morita's "Geometry of Differential Forms.")

Suhubi writes carefully, with close attention to detail. But one should be primed for this text by having read, say, Janich's "Vector Analysis" as an undergrad, or the even more basic book, Garrity's "Electricity and Magnetism for Mathematicians."

4 of 11 people found the following review helpful.
Dr.Ahmet Bakkaloglu...
By Ahmet Bakkaloglu
I know all the books written about differentiable forms and this one is the best one i have ever seen...I can not even compare this excellent book with the others...If you want to learn differentiable forms then you need this extraordinary book...You can find everything in this book in detail...Everthing is explained clearly...and you can find many good examples solved in detail..I am sure if you buy this book you will find that this book is much more better than your expectations..While reading the book you can see the authors great mastery and inteligence in each line..I am strongly recomended this book for everybody who wants to learn differential forms and to gain an experince to apply differential forms to physics and many other engineering problems. I want to thank the great Professor Erdogan Suhubi for this excellent book...

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Jumat, 17 Desember 2010

[J351.Ebook] Free PDF Effective Helping: Interviewing and Counseling Techniques 8th edition

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Sales Rank: #3481226 in Books
Published on: 1707
Binding: Paperback

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[O487.Ebook] PDF Ebook Computer Age Statistical Inference: Algorithms, Evidence, and Data Science (Institute of Mathematical Statistics Monographs), by Bradley E

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The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.

Sales Rank: #52111 in Books
Published on: 2016-07-21
Original language: English
Dimensions: 8.98" h x 2.01" w x 5.98" l, .0 pounds
Binding: Hardcover
495 pages

Review
"How and why is computational statistics taking over the world? In this serious work of synthesis that is also fun to read, Efron and Hastie, two pioneers in the integration of parametric and nonparametric statistical ideas, give their take on the unreasonable effectiveness of statistics and machine learning in the context of a series of clear, historically informed examples."
Andrew Gelman, Columbia University, New York

"This unusual book describes the nature of statistics by displaying multiple examples of the way the field has evolved over the past sixty years, as it has adapted to the rapid increase in available computing power. The authors' perspective is summarized nicely when they say, 'very roughly speaking, algorithms are what statisticians do, while inference says why they do them'. The book explains this 'why'; that is, it explains the purpose and progress of statistical research, through a close look at many major methods, methods the authors themselves have advanced and studied at great length. Both enjoyable and enlightening, Computer Age Statistical Inference is written especially for those who want to hear the big ideas, and see them instantiated through the essential mathematics that defines statistical analysis. It makes a great supplement to the traditional curricula for beginning graduate students."
Rob Kass, Carnegie Mellon University, Pennsylvania

"This is a terrific book. It gives a clear, accessible, and entertaining account of the interplay between theory and methodological development that has driven statistics in the computer age. The authors succeed brilliantly in locating contemporary algorithmic methodologies for analysis of 'big data' within the framework of established statistical theory."
Alastair Young, Imperial College London

"This is a guided tour of modern statistics that emphasizes the conceptual and computational advances of the last century. Authored by two masters of the field, it offers just the right mix of mathematical analysis and insightful commentary."
Hal Varian, Google

"Efron and Hastie guide us through the maze of breakthrough statistical methodologies following the computing evolution: why they were developed, their properties, and how they are used. Highlighting their origins, the book helps us understand each method's roles in inference and/or prediction. The inference-prediction distinction maintained throughout the book is a welcome and important novelty in the landscape of statistics books."
Galit Shmueli, National Tsing Hua University

"A masterful guide to how the inferential bases of classical statistics can provide a principled disciplinary frame for the data science of the twenty-first century."
Stephen Stigler, University of Chicago, and author of Seven Pillars of Statistical Wisdom

"Computer Age Statistical Inference offers a refreshing view of modern statistics. Algorithmics are put on equal footing with intuition, properties, and the abstract arguments behind them. The methods covered are indispensable to practicing statistical analysts in today's big data and big computing landscape."
Robert Gramacy, University of Chicago Booth School of Business

"Every aspiring data scientist should carefully study this book, use it as a reference, and carry it with them everywhere. The presentation through the two-and-a-half-century history of statistical inference provides insight into the development of the discipline, putting data science in its historical place."
Mark Girolami, Imperial College London

"Efron and Hastie are two immensely talented and accomplished scholars who have managed to brilliantly weave the fiber of 250 years of statistical inference into the more recent historical mechanization of computing. This book provides the reader with a mid-level overview of the last 60-some years by detailing the nuances of a statistical community that, historically, has been self-segregated into camps of Bayes, frequentist, and Fisher yet in more recent years has been unified by advances in computing. What is left to be explored is the emergence of, and role that, big data theory will have in bridging the gap between data science and statistical methodology. Whatever the outcome, the authors provide a vision of high-speed computing having tremendous potential to enable the contributions of statistical inference toward methodologies that address both global and societal issues."
Rebecca Doerge, Carnegie Mellon University, Pennsylvania

"In this book, two masters of modern statistics give an insightful tour of the intertwined worlds of statistics and computation. Through a series of important topics, Efron and Hastie illuminate how modern methods for predicting and understanding data are rooted in both statistical and computational thinking. They show how the rise of computational power has transformed traditional methods and questions, and how it has pointed us to new ways of thinking about statistics."
David Blei, Columbia University, New York

"Absolutely brilliant. This beautifully written compendium reviews many big statistical ideas, including the authors' own. A must for anyone engaged creatively in statistics and the data sciences, for repeated use. Efron and Hastie demonstrate the ever-growing power of statistical reasoning, past, present, and future."
Carl Morris, Harvard University, Massachusetts

"Computer Age Statistical Inference gives a lucid guide to modern statistical inference for estimation, hypothesis testing, and prediction. The book seamlessly integrates statistical thinking with computational thinking, while covering a broad range of powerful algorithms for learning from data. It is extraordinarily rare and valuable to have such a unified treatment of classical (and classic) statistical ideas and recent 'big data' and machine learning ideas. Accessible real-world examples and insightful remarks can be found throughout the book."
Joseph K. Blitzstein, Harvard University, Massachusetts

About the Author
Bradley Efron is Max H. Stein Professor, Professor of Statistics, and Professor of Biomedical Data Science at Stanford University, California. He has held visiting faculty appointments at Harvard University, Massachusetts, the University of California, Berkeley, and Imperial College of Science, Technology and Medicine, London. Efron has worked extensively on theories of statistical inference, and is the inventor of the bootstrap sampling technique. He received the National Medal of Science in 2005 and the Guy Medal in Gold of the Royal Statistical Society in 2014.

Trevor Hastie is John A. Overdeck Professor, Professor of Statistics, and Professor of Biomedical Data Science at Stanford University, California. He is coauthor of Elements of Statistical Learning, a key text in the field of modern data analysis. He is also known for his work on generalized additive models and principal curves, and for his contributions to the R computing environment. Hastie was awarded the Emmanuel and Carol Parzen prize for Statistical Innovation in 2014.

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56 of 57 people found the following review helpful.
Interesting, but too much overlap with their other books
By MsCurious
Their previous books include:
An Introduction to the Bootstrap (Chapman & Hall/CRC Monographs on Statistics & Applied Probability)
Large-Scale Inference: Empirical Bayes Methods for Estimation, Testing, and Prediction (Institute of Mathematical Statistics Monographs)
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
Statistical Learning with Sparsity: The Lasso and Generalizations (Chapman & Hall/CRC Monographs on Statistics & Applied Probability)
Roughly 50% of the book consists of abridged topics from the above. In particular, most of part III is machine learning topics that are presented better in Hastie's books. The deep learning chapter is just too brief to be useful. I with they cut these out and only included new insights.
Part I and some chapters from II and III are new. The comments about frequentist and Bayesian inference are illustrative. Same for James-Stein estimator. I was hoping for an update on bootstrap, but it's the same material, just with new presentation and simpler examples. All of this makes reading the book worthwhile.
The level of difficulty is similar to their other books. There are many examples and illustrations, but more could have been included for Bayesian multiple testing.

1 of 2 people found the following review helpful.
Totally awesome clear summary of the development of inference
By John Williams
Totally awesome clear summary of the development of inference, the current state of the art, and some intriguing speculation on where it will go in the future.

0 of 1 people found the following review helpful.
Perspectives about statistics missing in other expositions that has insiteful ...
By Abraham Silvers
Perspectives about statistics missing in other expositions that has insiteful looks at what statistics is all about historically and currently

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Selasa, 14 Desember 2010

[I710.Ebook] Fee Download BRIEF REVIEW MATH 2015 COMMON CORE GEOMETRY STUDENT EDITION GRADE 9/12, by PRENTICE HALL

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Sales Rank: #515391 in Books
Published on: 2014-12-12
Original language: English
Number of items: 1
Dimensions: 10.86" h x .70" w x 8.30" l, .0 pounds
Binding: Paperback

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Kamis, 09 Desember 2010

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Selasa, 07 Desember 2010

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Sales Rank: #895371 in Books
Published on: 2016-04-14
Original language: English
Binding: Hardcover

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