Urban Stormwater Management Planning with Analytical Probabilistic Models

Book Description

Understanding how to properly manage urban stormwater is a critical concern to civil and environmental engineers the world over. Mismanagement of stormwater and urban runoff results in flooding, erosion, and water quality problems. In an effort to develop better management techniques, engineers have come to rely on computer simulation and advanced mathematical modeling techniques to help plan and predict water system performance. This important book outlines a new method that uses probability tools to model how stormwater behaves and interacts in a combined- or single-system municipal water system. Complete with sample problems and case studies illustrating how concepts really work, the book presents a cost-effective, easy-to-master approach to analytical modeling of stormwater management systems.

Modeling the Internet and the Web: Probabilistic Methods and Algorithms

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Interesting book on Web modeling
A hopeless intellectual exercise
Excellent Book
Great book about relevant topic

Modeling the Internet and the Web: Probabilistic Methods and Algorithms
Pierre Baldi , Paolo Frasconi , and Padhraic Smyth
Manufacturer: Wiley
ProductGroup: Book
Binding: Hardcover

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Similar Items:

Pattern Classification (2nd Edition)
Mining the Web: Discovering Knowledge from Hypertext Data
Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management
Information Rules: A Strategic Guide to the Network Economy
Introduction to Data Mining, (First Edition)

ASIN: 0470849061

Book Description

Modeling the Internet and the Web covers the most important aspects of modeling the Web using a modern mathematical and probabilistic treatment. It focuses on the information and application layers, as well as some of the emerging properties of the Internet.

 Provides a comprehensive introduction to the modeling of the Internet and the Web at the information level.
 Takes a modern approach based on mathematical, probabilistic, and graphical modeling.
 Provides an integrated presentation of theory, examples, exercises and applications.
 Covers key topics such as text analysis, link analysis, crawling techniques, human behaviour, and commerce on the Web.

Interdisciplinary in nature, Modeling the Internet and the Web will be of interest to students and researchers from a variety of disciplines including computer science, machine learning, engineering, statistics, economics, business, and the social sciences.

"This book is fascinating!" - David Hand (Imperial College, UK)

"This book provides an extremely useful introduction to the intellectually stimulating problems of data mining electronic business." - Andreas S. Weigend (Chief Scientist, Amazon.com)

Customer Reviews:

Interesting book on Web modeling.......2005-01-15

An interesting book with a mathematical viewpoint on search, navigation, ecommerce and other aspects of the Web. Could be written more clearly in places. The competing text by Chakrabarti is a gentler introduction to the field. All the same, the review by 'unknown comic' below seems inaccurate. Although the book may be fairly technical in places, it should be accessible to students and researchers in fields like engineering, computer science, math, and statistics. Worth a look for quantitative researchers interested in what we can learn from Web data.

A hopeless intellectual exercise.......2004-12-12

I gave two stars to be charitable. The authors assume an incredible amount of background knowledge on behalf of the reader, in fact the book is more a review of this assumed knowledge rather than a tutorial or exposition of the topics. Equations and symbolic logic are incorporated needlessly to explain many simple concepts, but when applied to complex topics, these same rigorous techniques are given no supporting explanation. If you can follow the non-explanations the authors provide, you likely have already mastered this subject area and don't need to read this book at all. If you are new to the field, you will be stumped by the shallow coverage of complex theory, and this is even for people with graduate level computer science or mathematics. These authors need a better editor, someone who actually cares if the reader has any idea what is going on.

Excellent Book.......2003-08-01

This is the best book I have seen on the Web and the Internet. It is very thorough and covers topics ranging from Web graphs, to search engines, to customer behavior and ecommerce. It is up to date, well organized. I highly recommend it.

Great book about relevant topic.......2003-07-14

The internet and the web have become a wonderful field for applying mathematical methods to huge amounts of uncontrolled information. The ultimate contribution of this book, I think, is to present techniques by which results that are meaningful and useful for people -- such as text analysis and categorization, or relevance of web pages, or recommendation systems -- can emerge from the application of cold mathematical formulas. The mathematical tools are not necessarily new and may be hard to follow by non-experts (this is mostly Machine Learning, probability, etc). To fully appreciate the book, one needs to grasp the math, but I think the text is sufficiently interesting so that the book can also be enjoyed by people who don't like to read the math. The book is fairly comprehensive about modeling important processes that happen everyday on the web.

A Probabilistic Theory of Pattern Recognition (Stochastic Modelling and Applied Probability)

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May be the best pr book from a theoretical standpoint
An excellent but should be rated R.
Where's the beef? Right here!
deep and comprehensive

A Probabilistic Theory of Pattern Recognition (Stochastic Modelling and Applied Probability)
Luc Devroye , Laszlo Györfi , and Gabor Lugosi
Manufacturer: Springer
ProductGroup: Book
Binding: Hardcover

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Similar Items:

The Elements of Statistical Learning
Pattern Recognition and Machine Learning (Information Science and Statistics)
The Nature of Statistical Learning Theory (Information Science and Statistics)
Pattern Classification (2nd Edition)
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning)

ASIN: 0387946187

Book Description

Pattern recognition presents one of the most significant challenges for scientists and engineers, and many different approaches have been proposed. The aim of this book is to provide a self-contained account of probabilistic analysis of these approaches. The book includes a discussion of distance measures, nonparametric methods based on kernels or nearest neighbors, Vapnik-Chervonenkis theory, epsilon entropy, parametric classification, error estimation, free classifiers, and neural networks. Wherever possible, distribution-free properties and inequalities are derived. A substantial portion of the results or the analysis is new. Over 430 problems and exercises complement the material.

Customer Reviews:

May be the best pr book from a theoretical standpoint.......2004-04-12

In giving this book a second read, its importance finally dawned on me: it is one of the few if only books that provides a well-rounded theoretical (i.e. mathematical definitions and proofs) perspective on pattern recognition. Although other books, such as Duda et al's "Pattern Classification", have a significant degree of mathematical rigor, very few can claim to be based on the solid mathematical foundations of Lesbesgue measure theory, as this book is. This book has been a big inspiration for me, in that most pr papers I come across provide some method X, and show how experimentally it is more efficient or effective than methods Y and Z. Such papers, although possibly generating interest in the subject or method, do little if anything to advance the theory which in the end will have the final say of how, when, and why something works or when it doesn't. On the other hand, by making the assumption that the data comes from an unknown (i.e. nonparametric) probability measure space which induces an inherent optimal Bayesian error on the classification problem, this book shows how the theory of probability can be used to prove some very interesting results.

As an example, the authors define what it means to have a universally consistent classifier; i.e. a classifier which converges to the optimal Bayesian classifier as the amount of training data approaches infinity in the limit (irregardless of the data distribution). Moreover, one of the important results is that such classifiers exist and are often quite easy to devise (e.g. nearest-neighbor methods). And to be able to mathematically prove this is indeed inspiring.

In closing, I would highly recommend this book to anyone who has the mathematical prereqs (probability from an abstract measure-theory point of view)
and is interested in doing high quality mathematical research in pattern recognition. For that audience this book will provide a good foundation for literally an unlimited number of interesting questions; many of which remain unanswered.

For those who are more interested in the practice of pattern recogition, the above mentioned book by Duda et al. (ISBN 0471056693) will do just fine as a reference. The book "Pattern Recognition" by Theodoridis et al. is also of high quality (ISBN: 0126858756).

An excellent but should be rated R........2001-01-25

The book is great but the notations the authors employ will make you want to drop it on a first reading. Despite the generic title, it is really a reference book for the experts.

Issues in generalization are presented better in the book by Anthony and Bartlett but overall it is the best book available (for learning theorists).

Where's the beef? Right here!.......2000-09-14

This book provides a solid theoretical foundation for pattern recognition and statistical learning. If you consider yourself and expert, or want to be an expert in this field, this book is a must read. It will make you think hard about the concepts (and may be question whether you are or want to become an expert!).

deep and comprehensive.......1999-12-10

This is an awesome book, the best in-depth book on statistical classification to date. Filled with theorems and proofs on classical nonparametric techniques plus neural networks and learning. Standard reference for anybody doing serious pattern recognition and learning. Destined to become a classical reference in the field.

Probabilistic Modelling in Bioinformatics and Medical Informatics

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Probabilistic Modelling in Bioinformatics and Medical Informatics

Manufacturer: Springer
ProductGroup: Book
Binding: Hardcover

Biochemistry | Biological Sciences | Science | Subjects | Books

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Accessories:

Consumer Health Informatics: Informing Consumers and Improving Health Care (Health Informatics)
Aspects of Electronic Health Record Systems (Health Informatics)
Medical Informatics: Knowledge Management and Data Mining in Biomedicine (Integrated Series in Information Systems)

ASIN: 1852337788

Book Description

Probabilistic Modelling in Bioinformatics and Medical Informatics has been written for researchers and students in statistics, machine learning, and the biological sciences. The first part of this book provides a self-contained introduction to the methodology of Bayesian networks. The following parts demonstrate how these methods are applied in bioinformatics and medical informatics. All three fields - the methodology of probabilistic modeling, bioinformatics, and medical informatics - are evolving very quickly. The text should therefore be seen as an introduction, offering both elementary tutorials as well as more advanced applications and case studies.

Reliability: Probabilistic Models and Statistical Methods

Average customer rating:

The definitive introduction to reliability analysis
Excellent presentation of Reliability Math
Outstanding!

Reliability: Probabilistic Models and Statistical Methods
Lawrence Leemis
Manufacturer: Prentice Hall
ProductGroup: Book
Binding: Paperback

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ASIN: 0137205171

Book Description

Covers both the Probabalistic models and Statistical methods used by reliability engineers. It contains explanations of how the mathematical models and results apply to engineering design and the analysis of lifetime data sets. Most applications are drawn from mechanical, civil or electrical engineering systems.

Customer Reviews:

The definitive introduction to reliability analysis.......2001-02-01

This is THE seminal text on reliability analysis. The author writes with the same crystal clarity he uses to present material at technical conferences. The exercises are carefully graded to lead the diligent reader toward steadily deepening understanding of the material. All diagrams are clear, cogent, carefully annotated, and well keyed to accompanying text. The definition and explanations of cut sets are especially good, allowing the engineer or analyst to economically reduce a complex problem to a set of smaller, more mathematically tractable problems. Also, this work does an excellent job of ramping the reader's knowledge upward from the justifiably assumes prerequisite of basic statistics learned in one introductory class presumably having a calculus prerequisite.

Excellent presentation of Reliability Math.......2000-02-05

I have to give it to this author, he has a very high assumption of math. If you are not a math wiz but can comprehend Calculus, this book is beyond outstanding. I will give the reader the necessary math methods for achieving you reliability analysis correctly. I spent alot of time going through these calcs and doing the proofs they are with out a dought right on the money. The boo is packed full of wonderful examples and methods. Kudos to the author.

Outstanding!.......1999-10-13

This is definitely the best text on Reliability Engineering that I've seen. Leemis really brings the material to life in a way that I have not seen replicated in the other texts that I have perused. For a first introduction to Reliability, I cannot think of a better text. The reader should have a solid foundation in mathematical statistics, however, before starting on this volume. An adequate resource for building this foundation is Larson and Marx's "Introduction to Mathematical Statistics". Especially make sure you understand the basics of maximum-likelihood, as Leemis emphasizes it in his derivations -- the more advanced stuff you'll learn about in his book, however.

Book Description

With the growing complexity of engineered systems, reliability has increased in importance throughout the twentieth century. Initially developed to meet practical needs, reliability theory has become an applied mathematical discipline that permits a priori evaluations of various reliability indices at the design stages. These evaluations help engineers choose an optimal system structure, improve methods of maintenance, and estimate the reliability on the basis of special testing. Probabilistic Reliability Engineering focuses on the creation of mathematical models for solving problems of system design.

Broad and authoritative in its content, Probabilistic Reliability Engineering covers all mathematical models associated with probabilistic methods of reliability analysis, includingâunique to this bookâmaintenance and cost analysis, as well as many new results of probabilistic testing.

To provide readers with all necessary background material, this text incorporates a thorough review of the fundamentals of probability theory and the theory of stochastic processes. It offers clear and detailed treatment of reliability indices, the structure function, load-strength reliability models, distributions with monotone intensity functions, repairable systems, the Markov models, analysis of performance effectiveness, two-pole networks, optimal redundancy, optimal technical diagnosis, and heuristic methods in reliability. Throughout the text, an abundance of real world examples and case studies illustrate and illuminate the theoretical points under consideration.

For engineers in design, operations research, and maintenance, as well as cost analysts and R&D managers, Probabilistic Reliability Engineering offers the most lucid, comprehensive treatment of the subject available anywhere.

About the editor

JAMES A. FALK is Professor and Chairman of the Department of Operations Research at George Washington University. In addition to his numerous publications, Dr. Falk has lectured internationally as a Fulbright Lecturer.

Of related interest...

The reliability-testing "bible" for three generations of Eastern European scientists, adapted for Western scientists and engineers...

HANDBOOK OF RELIABILITY ENGINEERING

Originally published in the USSR, Handbook of Reliability Engineering set the standard for the reliability testing of technical systems for nearly three generations of applied scientists and engineers. Authored by a group of prominent Soviet specialists in reliability, it provides professionals and students with a comprehensive reference covering mathematical formulas and techniques for incorporating reliability into engineering designs and testing procedures. Divided into twenty-four self-contained chapters, the Handbook details reliability fundamentals, examines common reliability problems and solutions, provides a collection of computation formulas, and illustrates practical applications.

The Handbook's Russian editor and internationally recognized expert Igor A. Ushakov has joined with American engineering professionals to bring this indispensable resource to English-speaking engineers and scientists.

Book Description

In recent years considerable progress has been made in the area of probabilistic graphical models, in particular Bayesian networks and influence diagrams. Probabilistic graphical models have become mainstream in the area of uncertainty in artificial intelligence;
contributions to the area are coming from computer science, mathematics, statistics and engineering.

This carefully edited book brings together in one volume some of the most important topics of current research in probabilistic graphical modelling, learning from data and probabilistic inference. This includes topics such as the characterisation of conditional
independence, the sensitivity of the underlying probability distribution of a Bayesian network to variation in its parameters, the learning of graphical models with latent variables and extensions to the influence diagram formalism. In addition, attention is given to important application fields of probabilistic graphical models, such as the control of vehicles, bioinformatics and medicine.

The Craft of Probabilistic Modelling: A Collection of Personal Accounts (Applied Probability)

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The Craft of Probabilistic Modelling: A Collection of Personal Accounts (Applied Probability)

Manufacturer: Springer
ProductGroup: Book
Binding: Hardcover

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Probabilistic and Convex Modelling of Acoustically Excited Structures (Studies in Applied Mechanics)

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Probabilistic and Convex Modelling of Acoustically Excited Structures (Studies in Applied Mechanics)
Isaak Elishakoff , Y. K. Lin , and L. P. Zhu
Manufacturer: Elsevier Publishing Company
ProductGroup: Book
Binding: Hardcover

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Probabilistic Modelling
Isi Mitrani
Manufacturer: Cambridge University Press
ProductGroup: Book
Binding: Paperback

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ASIN: 0521585309

Book Description

Probabilistic modeling is the most cost-effective means of performance evaluation of computer systems. This book is a major revision of Modelling of Computer Communication Systems (CUP, 1987), one of the standard introductions to the area. Changes to the content reflect the change in the subject itself. Professor Mitrani has amplified the treatment of queues, reliability and applied probability. This book will be welcomed by students and teachers for its no-nonsense treatment of the basic results and examples of their application. The text includes the necessary fundamentals in probability and stochastic processes, making the book ideal for students in computer science or operations research taking courses in modern system design.

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