Modeling

The material contained in the textbook presents and discusses recent developments, but also important statistical (learning theory) concepts such as model selection, regularisation etc, in a unifying manner.
Although the authors are somewhat biased towards kernel methods, support vector machines in particular, they discuss the applicability and performance of other methods (neural networks, fuzzy systems, etc.). This is to be commended, as there are not many books that discuss all such methods in a common framework.
This book is highly recommended to readers wishing to gain a good understanding of the most significant statistical and other methods being applied in industry, and continuously experiencing significant academic research. A set of very good references (some mandatory and well known in the research community) presented at the end of each chapter directs the reader to some very useful material and scientific publications. This is a book that will particularly appeal to the research/academic community.

This book is excellent and easy to study. Graduate students will find the book statistical learning theory and support vector machines(SVMs),especially learning system based on recent advances in machine learning and multiobjective optimization. This book describes the Vapnik and Chervonenkis(VC) theory's generalization abilities. For statisticians, Applied mathematician, mechanical engineers and most graduate student are interested in reading this book. This is a very good excellent reference!!

The title of the book is somewhat misleading, in that most of the research papers involve advanced issues concerning one particular graphical model, namely the Bayesian network. For this reason I highly recommend, as a prerequisite to this book, Finn Jensen's "Bayesian Networks and Decision Graphs". Jensen's book is adequate in giving a good introduction and overview of the subject, but not sufficient for calling oneself an "expert" upon successfully digesting it.

To its credit, "Learning in Graphical Models" has several well-written and interesting papers, but the tutorial papers just did not seem enough of an introduction for me to feel comfortable using it as a first source of introduction.

What I find most compelling about Bayesian networks is the fact that they seem both highly modular (which facilitates reusability and network interconnectivity) and can be designed in a semi-rational manner (contrast this with neural-network architectures for which few good algorithms exist for determining size and number of layers). For this reason I imagine they will be important players in future engineering projects that require learning and adaptation.

My area of research revolves around graphical models... Best Book... The book that introduced me as to how effective graphical models are... As stated in the editorial review, graphical model is the marriage between graph theory and probability and this book justifies the sacredness of this marriage!

...for anyone interested in the history and development of Population Biology. It is a real shame that this book has gone out of print (and that Kingsland hasn't chosen to do a second edition)as this relatively short (267pp) book captured the really important trends and ideas of mathematical ecology up to the early 1970's in straight-forward and remarkably non-technical language. Kingsland gives us both the theories and the background and personalities that generated the theories, along with some delightful portraits of the Heavy Hitters during this seminal period in theoretical ecology. She ends with MacArthur & one would like to think that enough has happened since then that a sequel is in order, but I would recommend this book to any advanced undergrad or first-year grad student looking for background material.

Kingsland is biology by training therefore many scientific concepts in this book are very welled summarized and organized, making it easy for professionals and non-professional as well to grasp the general ideas in population biology. However, this book focus more on the historical context and the personality of some key scientists in this area, which gives readers more indepth understanding outside science itself. The auther did a wonderful job in interweaving science and human sides, and made it easy to pick up some major transitions in the history of population biology. highly recommmended to professional population ecologists, and general public who is interested in science as well! a bonus: some pictures of famous scientists in this area, such as McArthur, Lokta, Volttera etc...it's interesting to me, after reading all their work, finally had a chance to see what they look like.

William Wallace presents himself and science, philosophy, physics, and astronomy very well in this book. I can only imagine how many years of research it took to find all the information. There are five main sections to the first part of the book. In part 1, the Philosophy of Nature, Wallace explains how power comes from different parts of nature. Those being vegetative, animal, human and physical. Nature itself is in the form but Wallace's main historian in chapter one is Aristoltle. The four causes of every sensible reality are matter, form, agent and end. Nature acts towards an end. In chapter 2, Modeling the inorganic, Wallacefirst tries to describe the difference between an atom and a molecule. We are elements and compounds just like every other thing is on the earth, wether inorganic or organic. All of the elements in the periodic table are composed of the same elementary particles. Cosmology tells us how th universe came to be, which started with a certain protomatter. In chapter 3, Plant and animal structures, Wallace goes on to speak of species:are actula natural kinds thgat result from processes at work in nature and are therefore manifestations of nature itself. All living organisms derive their energy from the sun, and then metabolize, and engage in the essential feature of all living things-homeostasis. Also, there are added powers when organic (living) things come into play. Reproductive power, developmental power, homeostatic power and metabolic control. Animal powers go on to include external senses, internal senses, behavioral response and motor power. Chapter 4, the Modeling of the Mind, tries to emphasize that the use of mental representations is essential to understanding cognitive processes in animals and humans. Knowing has both an objective and subjective character. Aristotle's four internal senses: the central (common sense), the imagination, estiminative sense and memory are all part of the higher human powers. Chapter 5, Human nature, describes the only two faculties that make humans different from animals. The Intelect and the will. Part two of the entire text really is the magnificent part-years of research, organizing and selection have seemingly gone into trhe generation of this text. Chapter 6, Defining the philosophy of science, names all the idea makers of science chronologically. Bacon, Descartes, Locke, Hume, Kant, Whewell, J.S. Mill, Mach, Pierce, Poincare, Kuhn, Popper, Lakatos, Harvey, Newton etc. Chapters 8, 9, and 10 go deeper into proving the earth is round, how ellipses are formed, motion parallax and many other astonomical measures. Wallace truly proves that he is a philosopher of science-debating, cutting up, agreeing with the many different thinkers our time has allowed. How much longer canthe earth take all of this?

Fr Wallace has an wonderful little gem here. Reflecting his experience both as a physicist and a philospher in the tradition of Aquinas and Aristotle, Wallace demonstrates how Aristotelian philosophy of nature, that of form, prime matter, powers, etc. coalesces nicely with the current understanding of modern physics, biology, and chemistry. One need not be a science or philosophy major to follow Wallace; he does a very good job of relating scientific and philosophical concepts in a manner that makes them interesting to the layperson. Highly recommended for anyone interested in how ancient and medieval philosophy coincides with the discoveries of science and modern physics.

Bean is an original and careful thinker. His ideas challenge refutation. Those interested in physics, philosophy, and cybernetics will find this work to be a stimulating departure from traditional views on causality.

Bean's book certainly presents a fresh approach to reasoning about causation. His ideas on causation under time reversal and backward causation are interesting, indeed. The reader will find this work inclusive of physics, Artificial Intelligence and Philosophy quite stimulating.

Can statistical physics methods be used to help understand and predict the behaviors of groups of people over time? Aoki, a well-known expert in optimal control theory and dynamic models applied to economic models, suggests that they can. This book offers an inspiring medley of technical ideas and probabilistic models -- based on analogies to spin glasses, self-organizing physical systems, and key ideas of statistical mechanics -- to make plausible the idea that behaviors of aggregates of rational actors, too, can be predicted statistically. (Enthusiasts of Isaac Asimov's Foundation series, take note!)

The book is worth studying carefully for its ideas and models, even though its main thesis may not ultimately be convincing. It presents and illustrates modeling techniques that will be useful to marketing scientists and quantitative social scientists as well as to economists. A central idea is that individual behaviors can be represented by transitions among discrete choices or microstates, while the rates at which these transitions take place may depend on the frequency distribution of the whole population (and/or of local subpopulations)among microstates. The result is an adaptive process in which individual choices define the aggregate macrostate (i.e., the frequency distribution of individuals of various distinguished types among microstates), which in turn affects subsequent choices. Whether this adaptive process will reach any equilibrium, and, if so, which one and how and when, are principal concerns addressed through various well-analyzed models.

The techniques introduced and illustrated are primarily, partition function and stochastic process methods, including jump processes, diffusion processes, and martingale methods. Other ideas, from critical process theory, stochastic epidemics, large deviations, and maximum entropy econometrics and physics, are introduced and exploited where appropriate. The practical value of such methods to modelers cannot be denied. Aoki's main theme, that such techniques can be used to predict behaviors of populations of individuals, is not buttressed by any real applications. The models presented may be too simple to capture what many social scientists would consider essential real-world complexities, such as the generation of new player types and roles and the effects of boundaries and spatial distributions of resources and environments on populations over time. Nonetheless, the collection of ideas and methods presented, and the overall vision of a social science in which inter-individual heterogeneities and choices are accounted for by discrete transitions, makes this monograph exciting and valuable.

Note: Readers who enjoy this book may also want to read T.C. Schelling's Micromotives and Macrobehavior. Schelling's book is much less technical, but follows some of the same big ideas and applies them to many homely, compelling examples.

Content: 1 Introduction 2 Simple Illustrative and Motivating Examples 3 Empirical Distributions: Statistical Laws in Macroeconomics 4 Modeling Interactions I: Jump Markov Processes 5 Modeling Interactions II: Master Equations and Field Effects 6 Modeling Interactions III: Pairwise and Multiple-Pair Interactions 7 Sluggish Dynamics and Hierarchical State Spaces 8 Self-organizing and Other Critical Phenomena in Economic Models Elaborations and Future Directions of Research Appendix References Index

If you're looking for a book to study for the Novell 605 exam, this is a good book for getting started, but it comes up a little shy in errors and troubleshooting, good explanations of TCP utilities (telenet, etc.), and the like. But overall and excellent book.

If you want or need to learn the basics of TCP/IP, this is the book for you. Perfect for preparing for the Novell TCP/IP exam!! Unlike other study guides of its kind, Mr. Heywood doesn't try to be cute. No cakes get baked and nobody takes a walk down protocol lane. Clear and to-the-point explanation of everything you need to pass your exam!

First of all, this book screams cheap; with regards to it's level of quality, or lack of. It is printed on low-quality paper, using what appears to have been a dirty Xerox machine. Extremely poor choice of materials and process for a book that, by necessity, is rich in illustrations. Thankfully, though, youcan't judge a book by it's cover. If you want a fast-track start in I-deas, this is your book folks. Lots of action here. Twelve chapters take you from the basic concept all the way thru to assembling multiple components. Basic concepts are well explained in this book,and it could almost be titled: "Learn I-deas in 24 Hours",assuming approx. 2 hours per lesson. This book will NOT teach you all the fine details regarding I-deas design, nor does it appear that it will be a very usefull future reference source. What it IS is an exellent tool to get started with. I'm extremely pleased with what I got out of this book.

I think this book is fairly good, I need it!

An excellent book, especially for those who are now learning to use SPSS and the applications of statistical tests. The book however is lacking details for the advanced statistics - Loginlear analysis etc.More could have been said with regards to the data assumptions necessary to meet statistical analysis. Nevertheless it provides the reader with sufficient knowledge with regards to the use of SPSS, and is recommended for those who don't need to go indepth into multivariate analysis.

this book is great, i can recommend it to all who are autodidact and want to learn what spss can do with your data. it is easy to read and simple to follow and it has no formula, what is crucial to non-statisticians but seldomly thought about by those who write books on stats for users only. great stuff you have done here, authors!

I am a doctoral student who is currently working on two research projects. I found this book to be an excellent source for understanding the principals of quantitative research, and the application to SPSS. In the first section of the book Cramer sets up a study, and it is used in successive chapters in order to explain the various tests which can be run in SPSS. I have another Cramer book and find his writing to be clear and understandable, even with difficult concepts. The overarching problem with this book is it explains release 8. SPSS 10 is what is current and there is a huge difference between these two products. I have found this problematic on the application of SPSS, however, I gained a great deal in understanding SPSS testing and statistical concepts.

Terrefic introduction to SPSS. The fine print is value laden, specially for the hungry social scientist eager to see some meaning in his chaotic data. I read the book ear to ear and it taught me professionalism in the big big word of using computer technology through SPSS. The chapters particularly on creating new variables,Multivariate analysis and, Factor Analysis reduced a lot of my fears and apprehensions about Statistics in general. In fact the book has developed a new thirst in me to learn more. I'm truly indebted to the authors for a classic.