Book Description
This completely revised second edition presents an introduction to statistical pattern recognition. Pattern recognition in general covers a wide range of problems: it is applied to engineering problems, such as character readers and wave form analysis as well as to brain modeling in biology and psychology. Statistical decision and estimation, which are the main subjects of this book, are regarded as fundamental to the study of pattern recognition. This book is appropriate as a text for introductory courses in pattern recognition and as a reference book for workers in the field. Each chapter contains computer projects as well as exercises.
Customer Reviews:
A best book on Statistical Pattern Recognition.......2005-09-13
Multivariate analysis is borrowed to name a NEW subject, Statistical Pattern Recognition (SPR). Many statisticians think it unfair or a shame. In spite of these, it is a good reference book of SPR. :-)
[1] Many contents of this book can be found in any graduate textbook of Multivariate Analysis, for instance, Fisher's linear disciminant, etc.
[2] The book is badly printed. Why not using LaTeX?
[3] Guassian distribution is assumed here and there.
[4] It may be good as a reference book, but definitely not as a textbook.
Standard reference and a classic text but with flaws.......2004-01-20
I do not like to consult this book for the following, quite superficial reason. The book is sloppily produced and proofread
(and the fault is [probably] mainly the publisher's instead of the author's). This manifests itself, e.g., as follows
(1) the typography is flawed (the equations hurt at least my eyes);
(2) at its each appearance, the all-important >
< -sign goes the wrong way.
good coverage for engineers.......2000-08-04
Fukunaga is a standard source for pattern recognition methods often cited in the engineering literature. Covers parametric (particularly linear and quadratic discriminant algorithms) and nonparametric methods (density estimation). It is designed for and popular with engineers. When I was working at Nichols Research Corporation Fukunaga's papers and this book (earlier edition) were often cited as sources to justify the algorithms we used for discrimination problems. In fact Fukunaga had been a consultant to the company (used primarily by the Boston branch of the company where the KENN algorithms were developed). It is a reputable source. I still like Duda and Hart (1972) for good explanations of the fundamental concepts. For statisticians McLachlan's book is now far and away the best source.
Standard Reference in the Field.......2000-04-06
If you are writing a machine learning paper, and need to cite something to support an argument, you can almost always cite Fukunaga. This work is a standard reference in the field. The presentation of most material is very terse, but that is great if you already have a good feel for the material and need to look up some details about some algorithm or technique. There isn't much about neural networks here, but for the rest of the pattern recognition techniques, this is almost always the first place to start. Another strong point for this book is the use of realistic examples, which illustrate many of the statistical techniques.
Book Description
Linear models are central to the practice of statistics and form the foundation of a vast range of statistical methodologies. Julian J. Faraway's critically acclaimed Linear Models with R examined regression and analysis of variance, demonstrated the different methods available, and showed in which situations each one applies. Following in those footsteps, Extending the Linear Model with R surveys the techniques that grow from the regression model, presenting three extensions to that framework: generalized linear models (GLMs), mixed effect models, and nonparametric regression models. The author's treatment is thoroughly modern and covers topics that include GLM diagnostics, generalized linear mixed models, trees, and even the use of neural networks in statistics. To demonstrate the interplay of theory and practice, throughout the book the author weaves the use of the R software environment to analyze the data of real examples, providing all of the R commands necessary to reproduce the analyses. A supporting Web site at www.stat.lsa.umich.edu/~faraway/ELM holds all of the data described in the book. Statisticians need to be familiar with a broad range of ideas and techniques. This book provides a well-stocked toolbox of methodologies, and with its unique presentation of these very modern statistical techniques, holds the potential to break new ground in the way graduate-level courses in this area are taught.
Customer Reviews:
Flawed but well-explained.......2007-07-16
"Extending the Linear Model with R" is a "sequel" of sorts to the impressive "Linear Models with R" also written by Faraway. It assumes a basic knowledge of R (you don't have to be an expert) and a decent understanding of linear models. If you don't have that background, then I would start with the before-mentioned "Linear Models with R". If you read and understood that book, then you should be more than prepared for this one.
This book covers extensions of the linear model including Generalized Linear Models (GLM's), Mixed and Random Effects Models, Nonparametric Regression Models, Additive Models (including GAM's - Generalized Additive Models), and it contains a brief introduction to Regression Trees and Neural Networks. The biggest focus is on Generalized Linear Models. The book is fairly thorough, though not exactly comprehensive, in covering the topic of GLM's and specific commonly used GLM's. The material is very well-explained and easy to follow and they do a good job at integrating code, examples, and graphs in a way that facilitates understanding of both statistical concepts regarding GLM's and also the implementation of these concepts in R. The code is especially useful and it covers most things in R that you will need for this topic, at least those available from CRAN. The book is not very rigorous regarding theory, but that only makes the book easier to read and more practical. However, I do have one complaint regarding this section. The author spends several chapters discussing various commonly used GLM's and THEN finally gets around to defining what a GLM is and covering the basic theory. This seems backwards to me and for this reason I wouldn't read the chapters in order. Also, due to the late coverage of some of the basic theories, we don't get to see the implementation and analysis of certain sub-topics (e.g. leverage and influence) in the early examples.
Mixed and Random Effects models are second in terms of attention received. The organization is better and the explanations and code integration continue to be handled well. Nonparametric Regression and Additive Models only receive one chapter apiece, but both chapters are extremely informative and they are well-explained like the rest of the book. I was especially happy to see the coverage of GAM's (it's very short but useful) since it is a moderately recent topic (1990) and many similar books only make a brief mention of them (hey, GAM's exist) if they are mentioned at all. The chapter on Regression Trees is short, but again they make sure to cover many of the important sub-topics with clarity. The Neural Networks chapter is skimpy and you won't learn much, but it was an unexpected bonus so I can't take off points for that.
Do note that this book takes a regression approach throughout, so look elsewhere for an ANOVA perspective. The book is short with plenty of room left to talk about other topics. Thus, I would have liked to see a second part devoted to an ANOVA approach since I'm the kind of person who hates having to thumb through countless books, but they are open about the book's scope so I can't really complain.
Okay, one more complaint. I would have greatly liked to see an appendix of the R functions used throughout the book with short descriptions and references to where in the book you can find the function being discussed. R Help isn't bad, so it's not a tragic omission, but it still would have been nice.
In summary, this book is extremely useful if you plan on using extensions of linear models with R. Flaws aside, it receives my recommendation.
Average customer rating:
- one of the best introduction to probability and stochastic processes
- Why are there so many examples?
- One of the most accessible and engaging text books I've read
- very good
- Good development of intuition, but not as good for other purposes...
|
Introduction to Probability Models, Eighth Edition
Sheldon M. Ross
Manufacturer: Academic Press
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ASIN: 0125980558 |
Book Description
Introduction to Probability Models, 8th Edition, continues to introduce and inspire readers to the art of applying probability theory to phenomena in fields such as engineering, computer science, management and actuarial science, the physical and social sciences, and operations research. Now revised and updated, this best-selling book retains its hallmark intuitive, lively writing style, captivating introduction to applications from diverse disciplines, and plentiful exercises and worked-out examples.
The 8th Edition includes five new sections and numerous new examples and exercises, many of which focus on strategies applicable in risk industries such as insurance or actuarial work.
The five new sections include:
* Section 3.6.4 presents an elementary approach, using only conditional expectation, for computing the expected time until a sequence of independent and identically distributed random variables produce a specified pattern.
* Section 3.6.5 derives an identity involving compound Poisson random variables and then uses it to obtain an elegant recursive formula for the probabilities of compound Poisson random variables whose incremental increases are nonnegative and integer valued
* Section 5.4.3 is concerned with a conditional Poisson process, a type of process that is widely applicable in the risk industries
* Section 7.10 presents a derivation of and a new characterization for the classical insurance ruin probability.
* Section 11.8 presents a simulation procedure known as coupling from the past; its use enables one to exactly generate the value of a random variable whose distribution is that of the stationary distribution of a given Markov chain, even in cases where the stationary distribution cannot itself be explicitly determined.
Other Academic Press books by Sheldon Ross:
Simulation 3rd Ed., ISBN:0-12-598053-1
Probability Models for Computer Science, ISBN 0-12-598051-5
Introduction to Probability and Statistics for Engineers and Scientists, 2nd Ed., ISBN: 0-12-598472-3
* Classic text by best-selling author
* Continues the tradition of expository excellence
* Contains compulsory material for Exam 3 of the
Society of Actuaries
Customer Reviews:
one of the best introduction to probability and stochastic processes.......2007-08-20
Understanding probability requires various resources to read. I think this book is one of the irreplaceable element in these resources. It is an introduction book as the name implies. Examples are illuminating the subject very well.
Why are there so many examples?.......2007-04-01
Extremely difficult to dig through the excessive examples in order to find the relevant theorems and results. Because of this, the problems at the end of each chapter become exercises in tedium, as more time is spent searching for the necessary theorems in the text than in actually working out the solution.
I do not recommend.
One of the most accessible and engaging text books I've read.......2007-02-16
During my undergraduate career I've had the opportunity to spend several thousand dollars on textbooks--many of which have pertained to mathematics in some way. Most of these books, including those concerned with statistics and probability, have been interested in either delivering pure theory or an unending supply of problem sets (with little or nothing in the way of instructive content). Thankfully Ross's book defies these conventions.
By presenting the material in large sets of well explained and genuinely interesting problems, the book avoids being bogged down by excessive theory or volumes of sterile exercises. As a result, the book is unusually easy to read, and quite useful when it comes to clarifying or augmenting what has been taught in class.
very good.......2006-11-14
I used this book for a graduate-level course in Stochastic Processes taught by Dr. Sheldon Ross himself. I must say that I never liked probability and stochastics until I read this book. Reading it is a pleasure! The topics are presented in a highly methodical manner, with plenty of examples and exercises. The exercises are presented in a gradation. Covers a wide range of topics, and is very helpful for a course in stochastics, especially for a student who doesn't have a strong background in P & SP. This is the book to own, don't miss it!
Good development of intuition, but not as good for other purposes..........2006-09-28
I have many of the same criticisms of this book that I do of Ross's book titled: "Probability: a first course". This book reviews most of the material from that book at a faster pace and then goes into other topics. Ross in the introduction states that his main goal in this text is to develop the reader's intuition for probabilistic reasoning. This book is excellent towards acheiving this goal, but not very good for anything else. It is a very "pure" probability text, completely ignoring the fact that the field of statistics exists and is useful. At the same time, it is somewhat weak on theory. Measure theory isn't mentioned, and the emphasis overall is on computation and problem solving, not proving theorems and understanding theoretical connections between different ideas.
This book has too many examples and not enough discussion. While the examples are usually well-executed, and while I think examples are important in probability, I think it's also important to talk about the abstract development of the subject. In my opinion, more prose and fewer examples would improve the quality of this text.
Another criticism I have of this book is that this book focuses exclusively on probability, refusing to touch statistics even with a ten foot pole. While this in itself is fine, I think this book misses numerous chances to pave the road towards the later study of mathematical statistics. The result is that someone reading this book will not be particularly well prepared for studying statistics, even though the fields of probability and statistics are intimately tied to each other.
Lastly, this book has gone through too many editions--one of the reasons I rated it 3 stars instead of 4 is that I believe that there has been almost no noticeable improvement in the last two editions (I have not read any farther back than that so I can't say more). I think this is a money-making scheme on behalf of the publisher, and I think this reflects poorly on the author and publisher alike.
Average customer rating:
- Good in some ways but
- Very good textbook for (non)linear mixed models in R
- As someone who just learn R
- R and S, The best in statistical analysis
- well written account of mixed models with SPlus software
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Mixed Effects Models in S and S-Plus
Jose C. Pinheiro , and
Douglas M. Bates
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ASIN: 0387989579 |
Book Description
This book provides an overview of the theory and application of linear and nonlinear mixed-effects models in the analysis of grouped data, such as longitudinal data, repeated measures, and multilevel data. Over 170 figures are included in the book.
Customer Reviews:
Good in some ways but.......2006-08-07
It is difficult to understand who this book is written for. The authors are clearly clever guys but strangely the book does little to explain the command structure for modelling in the authors' own package! It would be useful to explain the models in the context of random effects/nested modelling in conventional ANOVA for newbies. Please authors if you do a second edition show how really simple models are programmed.
A good example of its beginner unfriendliness is the first example(!) to have a vector within a dataframe of exactly the same name. Not a good idea in a text book. You would have thought the editor would have had something to say about that.
Very good textbook for (non)linear mixed models in R.......2006-07-25
Even though the title of this book is mixed effects models in S and S+ but this is a wonderful book for a person to learn mixed effect models in R. If you read this book carefully and also use the R to practice examples. Then you will get a lot from the learning process. Of course you should has a basic background in linear model before you read this book.
I strong recommend this book to whom needs nonlinear mixed models of longitudinal data in R.
Every statistician should has this book.
As someone who just learn R.......2006-01-19
At first sight, there are a lot of SPlus/R commands in the book which one may expect to learn a lot about using nlme. However, I found there is a lack in explanation of the command, if not missing. For e.g., in Chapter 1, the book talks about nested classficification models and gave the command in Splus/R, with the model equation right in front of me, I still can't figure out why in the command ...... random=list(Dog=~day,Side=~1) .... can't figure out the logic of this command in relation to the equation. I know this is not an introductory book for R, but a lot of time, when we want to use R or Splus the first time, it's not b'cos we want to do simple statistics, so a bit more explanation of the commands will be helpful, rather than following the commands blindly. Furthermore, I'm not even talking about R programming. Having said that, I still want to emphasize it is a good book written for the topic and package.
R and S, The best in statistical analysis.......2004-01-16
The book has excelent presentation (theory and practical), overall a lot applications with R (my favorite)...If you want to be update in applied statistics, in my opinion, you should have it...
well written account of mixed models with SPlus software.......2001-04-22
Mixed effects linear models are very useful particularly in medical research (e.g. device or drug trials). Pinheiro and Bates provide comprehensive cover of both linear and nonlinear mixed effects models with many applications. Implementation is illustrated using the S programming language and the software package SPlus.
Bates is an expert on nonlinear regression and hence the emphasis on the nonlinear models as well as the linear ones.
Book Description
Now in widespread use, generalized additive models (GAMs) have evolved into a standard statistical methodology of considerable flexibility. While Hastie and Tibshirani's outstanding 1990 research monograph on GAMs is largely responsible for this, there has been a long-standing need for an accessible introductory treatment of the subject that also emphasizes recent penalized regression spline approaches to GAMs and the mixed model extensions of these models. Generalized Additive Models: An Introduction with R imparts a thorough understanding of the theory and practical applications of GAMs and related advanced models, enabling informed use of these very flexible tools. The author bases his approach on a framework of penalized regression splines, and builds a well-grounded foundation through motivating chapters on linear and generalized linear models. While firmly focused on the practical aspects of GAMs, discussions include fairly full explanations of the theory underlying the methods. Use of the freely available R software helps explain the theory and illustrates the practicalities of linear, generalized linear, and generalized additive models, as well as their mixed effect extensions. The treatment is rich with practical examples, and it includes an entire chapter on the analysis of real data sets using R and the author's add-on package mgcv. Each chapter includes exercises, for which complete solutions are provided in an appendix. Concise, comprehensive, and essentially self-contained, Generalized Additive Models: An Introduction with R prepares readers with the practical skills and the theoretical background needed to use and understand GAMs and to move on to other GAM-related methods and models, such as SS-ANOVA, P-splines, backfitting and Bayesian approaches to smoothing and additive modelling.
Book Description
Copulas are functions that join multivariate distribution functions to their one-dimensional margins. The study of copulas and their role in statistics is a new but vigorously growing field. In this book the student or practitioner of statistics and probability will find discussions of the fundamental properties of copulas and some of their primary applications. The applications include the study of dependence and measures of association, and the construction of families of bivariate distributions. With nearly a hundred examples and over 150 exercises, this book is suitable as a text or for self-study. The only prerequisite is an upper level undergraduate course in probability and mathematical statistics, although some familiarity with nonparametric statistics would be useful. Knowledge of measure-theoretic probability is not required. Roger B. Nelsen is Professor of Mathematics at Lewis & Clark College in Portland, Oregon. He is also the author of "Proofs Without Words: Exercises in Visual Thinking," published by the Mathematical Association of America.
Customer Reviews:
Note: the paperback edition is out of date.......2006-08-15
I just got this book, so I can't comment on the contents yet. However, I feel quite ripped off, because this is the previous edition. The hardcover, which is only $20 more is a new second edition, while the paperback is from 1999.
Copula theory - an excellent introduction.......2001-07-09
This is a great introduction to the area for those already possessing good mathematical ability and knowledge of distribution theory. All the seminal theorems and references are in here and the reader would be wise to check them out. Well written and communicated, didn't find any typo's. Enjoyed it. This area is fast growing in the area of mathematical finance.
Amazon.com
You could just as easily call this book How to Bet at Jai-Alai and Win! But that's only half the story. While Calculated Bets might indeed help you make a buck down at the fronton, it's as much concerned with the power of mathematical modeling and computer programming. The story of accomplished mathematician Steven Skiena's longtime obsession with this obscure Basque sport, Calculated Bets uses straightforward mathematics and real-world examples to divine the statistical mysteries behind playing--and, more important, wagering on--jai alai. (Which goes a long way toward explaining why Cambridge University Press is publishing what's basically a book about gambling.)
A self-styled "mild-mannered professor," the conversational Skiena (The Algorithm Design Manual) delivers on his book's many promises, from explaining how mathematical models are "designed, built, and validated" to providing lucid discussions of such topics as market efficiency and the difference between correlation and causation. Even better are his riffs on why real programmers hate Microsoft (hint: it's not jealousy) and the beauty behind interesting curves. In the end, Skiena even puts his money where his mouth is: using a modem, he sets loose an auto-dialing program called Maven that he and his grad students cooked up, sending it off in the wee hours of the morning to cull the Web for stats, play each match a half-million times, and then automatically wager a $250 stake. --Paul Hughes
Book Description
Calculated Bets describes a gambling system that works. Steven Skiena, a jai-alai enthusiast and computer scientist, documents how he used computer simulations and modeling techniques to predict the outcome of jai-alai matches and increased his initial stake by 544% in one year. Skiena demonstrates how his jai-alai system functions like a stock trading system, and includes examples of how gambling and mathematics interact in program trading systems, how mathematical models are used in political polling, and what the future holds for Internet gambling. With humor and enthusiasm, Skiena explains computer predictions used in business, sports, and politics, and the difference between correlation and causation. An unusual presentation of how mathematical models are designed, built, and validated, Calculated Bets also includes a list of modeling projects with online data sources. Steven Skiena, Associate Professor of Computer Science at SUNY Stony Brook, is the author of The Algorithm Design Manual (Springer-Verlag, 1997) and the EDUCOM award-winning Computational Discrete Mathematics. He is the recipient of the ONR Young Investigator's Award and the Chancellor's Award for Excellence in Teaching at Stony Brook. His research interests include discrete mathematics and its applications, particularly the design of graph, string, and geometric algorithms.
Download Description
This is a book about a gambling system that works. It tells the story of how the author used computer simulations and mathematical modeling techniques to predict the outcome of jai-alai matches and bet on them successfully - increasing his initial stake by over 500% in one year! His results can work for anyone: at the end of the book he tells the best way to watch jai-alai, and how to bet on it. With humor and enthusiasm, Skiena details a life-long fascination with computer predictions and sporting events. Along the way, he discusses other gambling systems, both successful and unsuccessful, for such games as lotto, roulette, blackjack, and the stock market. Indeed, he shows how his jai-alai system functions just like a miniature stock trading system. Do you want to learn about program trading systems, the future of Internet gambling, and the real reason brokerage houses don't offer mutual funds that invest at racetracks and frontons? How mathematical models are used in political polling? The difference between correlation and causation? If you are curious about gambling and mathematics, odds are this book is for you!
Customer Reviews:
Non Fiction.......2007-09-03
A university guy looks at mathematically modelling a local sporting event to see if he can beat the odds. He discovers some ineffiencies because of the structure of the game of jai-alai. It is very small stakes betting, but he does come up with something that works.
However, being parimutuel, with very small pools, if there were ever two people doing the same thing at the same time it wouldn't work for anyone.
You want this, even if you don't know you want this........2005-06-07
Steven Skeina, Calculated Bets (Cambridge University Press, 2001)
The first thing you need to know about Calculated Bets is that it is, by far, the most readable book you will ever pick up from Cambridge University Press. One wonders, in fact, how Skeina got past the stuffiness factor that distinguishes so much academic publishing to get this book released. A distinguished university putting out a book on, for all intents and purposes, building a system to bet jai-alai? And yet, I know it exists, as I have held it in my hands and read it.
And a good read it is, too. Skeina takes a look at what may be America's most overlooked and underrated spectator sport and how he created a computer program to automatically bet on jai-alai that actually beat the game (and the book's major failing, in my opinion, is that he didn't get farther into the actual algorithms he used), and uses it as an introduction to jai-alai and an introduction to theoretical programming at the same time. It's not a book for programming junkies as, as I alluded to, you're not going to get anything even remotely resembling hard code. It's also not really a book for handicapping devotees, because while Skeina does talk briefly about the basics of the stuff he plugged into those algorithms, he's going to leave you to do all the real work. And yet, despite both of these things, I loved this book. It may just be the novelty of reading something non-fiction from a University press that actually didn't require having a dictionary next to me (I should note here that much of what I read from university presses is linguistic and literary theory translated from obscure Eastern European languages, and poetry that might as well have been written in those languages and remains untranslated). Skeina has produced an enjoyable piece of work that seems almost marketless. That is a shame, because it's a fun book, and well worth reading.
For Both Jai Alai and Computer Enthusiasts.......2005-01-24
This book documents a simple computer program written by the author to exploit statistical advantages made through the Spectacular Seven scoring system for jai alai. The book is a well written summary of the author's interest in the game which lead him to this project. Like the author, I was an avid jai alai fan in the late 80s before the player's strike destroyed the game. The author matched his interest in jai alai and programming to lead graduate students to write subroutines for a Monte Carlo simulation computer program which won him money before he claims to have abandoned the project. The author is a gifted writer for an otherwise dry text as he keeps reasonable amount of humor and style which maintains your interest.
Mathematical modeling done right.......2002-12-30
To knowledge seekers, the ability to understand and beat a system is the entire game. In this book, Skiena describes how he and some of his students wrote a computer program to win money betting on professional jai alai matches. Along the way, he explains the origins of the game and some of the basic rules, the fundamental bets that can be made as well as the meaning of statements such as pari-mutuel betting. His program does work well, in that he quadruples his money in a short time. Once that is done, he gives the money to a university charity, hoping to make his money from writing this book.
The fact that such a program could be created is not surprising. Jai-alai is a sport where individuals compete one-on-one or in teams of two, and the betting patterns determine the payoffs. It is much easier to simulate these types of matchups and predict the outcome than it is for team games. Baseball managers have been doing such modeling for years. If my memory serves me correctly, the first to do it in major league baseball was Davey Johnson, who kept detailed statistics on all pitcher-batter matchups. All of his decisions concerning who to put up to bat were then based on playing the percentages. That is essentially what Skiena does, although with a different twist. Pari-mutuel betting is where those who wager are betting against each other, so the patterns of wagering determine the payoffs. The patterns of betting are also factored into his predictions. These conditions make it possible for someone to make money creating such a system, but only as long as no one else is doing it. If others begin to use the same system, then the players are betting against each other, destroying the opportunity to make a profit. Therefore, his very act of publishing this book probably means that his system can no longer be used to win at jai-alai betting.
This is an excellent example of how basic mathematical modeling is done. Use data of previous results to form a model of what has happened in order to predict what will happen. Skiena writes with a wit and rigor that is rarely seen in mathematics. Very little mathematics background is needed in order to understand the explanations of the behavior of the program and why it works.
I found this book so interesting that I stayed up very late finishing it. It reads like a novel, but teaches you a lot about mathematics. Instructors in mathematical modeling and computer programming can find many interesting ideas for classroom exercises in it. As long as no one takes it too seriously, it is all in good, clean fun.
An Interesting Mathematical Tale.......2002-02-18
It's an enjoyable read. Pretty light on mathematics and software engineering though. You can easily get through this book in an evening or two and refresh some of your thoughts on modeling and statistics. Steven Skiena keeps a web site ...that's worth a peek and has reading material on this work there. Wish the book had shipped with a CD though so you could play around with his model and simulate a few games of Jai Alai for fun.
Average customer rating:
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Disease Evolution: Models, Concepts, and Data Analyses (Dimacs Series in Discrete Mathematics and Theoretical Computer Science)
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ASIN: 0821837532 |
Book Description
Infectious diseases are continuing to threaten humankind. While some diseases have been controlled, new diseases are constantly appearing. Others are now reappearing in forms that are resistant to drug treatments. A capacity for continual re-adaptation furnishes pathogens with the power to escape our control efforts through evolution. This makes it imperative to understand the complex selection pressures that are shaping and reshaping diseases. Modern models of evolutionary epidemiology provide powerful tools for creating, expressing, and testing such understanding.
Bringing together international leaders in the field, this volume offers a panoramic tour of topical developments in understanding the mechanisms of disease evolution. The volume's first part elucidates the general concepts underlying models of disease evolution. Methodological challenges addressed include those posed by spatial structure, stochastic dynamics, disease phases and classes, single- and multi-drug resistance, the heterogeneity of host populations and tissues, and the intricate coupling of disease evolution with between-host and within-host dynamics. The book's second part shows how these methods are utilized for investigating the dynamics and evolution of specific diseases, including HIV/AIDS, tuberculosis, SARS, malaria, and human rhinovirus infections.
This volume is particularly suited for introducing young scientists and established researchers with backgrounds in mathematics, computer science, or biology to the current techniques and challenges of mathematical evolutionary epidemiology.
Copublished with the Center for Discrete Mathematics and Theoretical Computer Science beginning with Volume 8. Volumes 1-7 were copublished with the Association for Computer Machinery (ACM).
Book Description
Markov random field (MRF) theory provides a basis for modeling contextual constraints in visual processing and interpretation. It enables us to develop optimal vision algorithms systematically when used with optimization principles. This book presents a comprehensive study on the use of MRFs for solving computer vision problems. The book covers the following parts essential to the subject: introduction to fundamental theories, formulations of MRF vision models, MRF parameter estimation, and optimization algorithms. Various vision models are presented in a unified framework, including image restoration and reconstruction, edge and region segmentation, texture, stereo and motion, object matching and recognition, and pose estimation. This second edition includes the most important progress in Markov modeling in image analysis in recent years such as Markov modeling of images with "macro" patterns (e.g. the FRAME model), Markov chain Monte Carlo (MCMC) methods, reversible jump MCMC. This book is an excellent reference for researchers working in computer vision, image processing, statistical pattern recognition and applications of MRFs. It is also suitable as a text for advanced courses in these areas.
Book Description
Stochastic Subsurface Hydrogeology is the study of subsurface, geological heterogeneity, and its effects on flow and transport process, using probabilistic and geostatistical concepts. This book presents a rational, systematic approach for analyzing and modeling subsurface heterogeneity, and for modeling flow and transport in the subsurface, and for prediction and decision-making under uncertainty. The book covers the fundamentals and practical aspects of geostatistics and stochastic hydrogeology, coupling theoretical and practical aspects, with examples, case studies and guidelines for applications, and provides a summary and review of the major developments in these areas.
Customer Reviews:
A great book.......2004-09-17
This is a great book, for sure the best and most comprehensive book on stochastic hydrogeology available today. It covers nearly all the fundamental and practical aspects of stochastic hydrogeology, with emphasis on both the theoretical and practical aspects of the discipline. The language is simple, with many examples and case studies. The book is a great reference for scientists who are familiar with stochastic hydrogeology as well as for students and/or practitioners who may get informed about the discipline and learn how to implement the various tools available. The book is at the same time a very good introduction to the matter and a reference book for people who are already familiar with stochastic hydrogeology and want to keep updated with the most recent developments. This is the kind of book to keep on the desk.
An excellent textbook!.......2004-06-20
Applied Stochastic Hydrogeology is easily the best book of this century in its field. Its intuitive and down-to-earth style makes even the most intricate aspects of stochastic analyses readily accessible to both graduate students and active researchers. The subjects the book covers range from stochastic site characterization and image reconstruction from sparse data to the concept of effective hydraulic parameters and probabilistic assessment of flow and transport in heterogeneous environments.
Books:
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- Linear Algebra with Applications
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- Little, Brown Essential Handbook, The (5th Edition)
- Many Particle Physics (Physics of Solids and Liquids)
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- Mathematical Methods and Algorithms for Signal Processing
- Mathematical Methods and Algorithms for Signal Processing
- Mathematical Methods and Algorithms for Signal Processing
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