Showing posts with label science. Show all posts
Showing posts with label science. Show all posts

Introduction to Probability Models, Ninth Edition Review

Introduction to Probability Models, Ninth Edition
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The first four chapters alone (intro, random variables, conditonal probability, markov chains) are worth the price of the book. The author packs each chapter with very interesting examples and problems. The one I found most interesting was his probabilistic analysis of the 2-SAT and SAT problems of computer science. Here he gives an informal math argument as to why 2-SAT is polynomial time decidable and why SAT should be intractable.
On the other hand, I think someone relatively new to probability theory may find his neat problems and examples a bit too much with a first reading. The book is in its seventh edition, and I think Ross has taken advantage of this by providing newer insights and more interesting problems, but in doing so it may overwhelm the novice.
If you are learning probability for the first or second time, I recommend you supplement this book with Roussas "A Course in Mathematical Statistics". Despite its title, the first 9 chapters give a calculus-based intro to probability. And the rest of the book is *excellent* for a calculus-based intro to statistics.

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Negative Binomial Regression Review

Negative Binomial Regression
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The first edition of this book was one of the first on this topic. The text is is very comprehensive, covering count models in general, the common Poisson regression model and its generalization to over-and-under dispersion with all the various forms of the negative binomial regression model. The Poisson distribution has the property that its mean and variance are the same. When sample estimates of variance are significantly higher (lower) than the estimated mean, the model is said to be overdispersed (underdispersed).
The main additions in the second edition of the book are the advances in software to estimate parameters of the various negative binomial models. Hilbe describes the currently available software in SAS, SPSS and STATA as well as the econometric package LIMDEP.
The book covers the historical development of the negative binomial regression model. It is primarily an applied text with numerous examples and demonstration of the various software products. As with all of Joe Hilbe's books, this text is thorough and scholarly with an extensive list of references. Important theorems and other theoretical results are given but are presented to be imformative rather than to develop and teach the theory. The text is well-written and for the most part easy to understand. Emphasis is on computation and goodness of fit of the models. Although both overdispersion and underdispersion are covered overdispersion is emphasized as Hilbe sees it as the most common departure from the Poisson model.

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This second edition of Hilbe's Negative Binomial Regression is a substantial enhancement to the popular first edition. The only text devoted entirely to the negative binomial model and its many variations, nearly every model discussed in the literature is addressed. The theoretical and distributional background of each model is discussed, together with examples of their construction, application, interpretation and evaluation. Complete Stata and R codes are provided throughout the text, with additional code (plus SAS), derivations and data provided on the book's website. Written for the practising researcher, the text begins with an examination of risk and rate ratios, and of the estimating algorithms used to model count data. The book then gives an in-depth analysis of Poisson regression and an evaluation of the meaning and nature of overdispersion, followed by a comprehensive analysis of the negative binomial distribution and of its parameterizations into various models for evaluating count data.

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Water Quality: Diffuse Pollution and Watershed Management, 2nd Edition Review

Water Quality: Diffuse Pollution and Watershed Management, 2nd Edition
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This is a great book for dealing with Non-Point Source (NPS) pollution. It covers a fairly comprehensive list of topics, while not going into great depth on most. It gives you enough information to get a good start on just about ANY analysis related to NPS pollution and a solid beginning to expand your research from if more in-depth analysis is required. In my opinion, this is a great reference for anyone working in water quality, and a "must-have" for working with NPS.

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Strategic Asset Allocation in Fixed Income Markets: A Matlab based user's guide (The Wiley Finance Series) Review

Strategic Asset Allocation in Fixed Income Markets: A Matlab based user's guide (The Wiley Finance Series)
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Quoting the Soviet worker who wrote to The Pravda to condemn "Dr. Zhivago": "I have not read the book, but I deplore it". The table of contents left me puzzled. Term-structure models are dealt with over just 18 pages - this seems to satisfy all the five-star reviewers - after nothing more than fixed-income basics, there's talk of CAPM (is that the proposed approach to fixed-income asset allocation?), a why-these-particular-topics foray into econometrics, and some novice-oriented Matlab content. (GUIs?) I am struggling to see the book as a credible reference on the subject of its title.


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Matlab is used within nearly all investment banks and is a requirement in most quant job ads. There is no other book written for finance practitioners that covers this
Enables readers to implement financial and econometric models in Matlab
All central concepts and theories are illustrated by Matlab implementations which are accompanied by detailed descriptions of the programming steps needed
All concepts and techniques are introduced from a basic level
Chapter 1 introduces Matlab and matrix algebra, it serves to make the reader familiar with the use and basic capabilities if Matlab. The chapter concludes with a walkthrough of a linear regression model, showing how Matlab can be used to solve an example problem analytically and by the use of optimization and simulation techniques
Chapter 2 introduces expected return and risk as central concepts in finance theory using fixed income instruments as examples, the chapter illustrates how risk measures such as standard deviation, Modified duration, VaR, and expected shortfall can be calculated empirically and in closed form
Chapter 3 introduces the concept of diversification and illustrates how the efficient investment frontier can be derived - a Matlab is developed that can be used to calculate a given number of portfolios that lie on an efficient frontier, the chapter also introduces the CAPM
Chapter 4 introduces econometric tools: principle component analysis is presented and used as a prelude to yield-curve factor models. The Nelson-Siegel model is used to introduce the Kalman-Filter as a way to add time-series dynamics to the evolution of yield curves over time, time series models such as Vector Autoregression and regime-switching are also presented
Supported by a website with online resources - www.kennyholm.com where all Matlab programs referred to in the text can be downloaded. The site also contains lecture slides and answers to end of chapter exercises


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A First Course in Stochastic Processes, Second Edition Review

A First Course in Stochastic Processes, Second Edition
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A remarkable well organised work. Every chapter contains all needed definitions and formulas, deep discussions of their meanings, proofs, and examples, all extraordinarily well blended. Also every chapter has two set of problems. The 'elementary problems' require applying the material covered. The 'problems' require to prove results, they provide an excellent ground to develop this skill. Some times the classic format proof-theorem is used, but usually the ideas flow: starting with a problem, introducing necessary definitions and finding a solution eventually a theorem is stated as a natural consequence.
The writing style is similar to the immortal 'Introduction to Probability Theory' and its Applications' by Feller, with a similar mixture of rigorous mathematics and probabilistic intuition. Though 'A First Course...' only reviews the basics, it has some common topics with Feller's and covers more advanced topics.
The style of the book is the perfect opposite of 'Introduction to probability Models' by Sheldon Ross, which is written in a much more flamboyant style, full of surprises and amazement, and requires the constant use of pencil and paper to follow the developments. These two sources can be combined to master the subject, despite the fact that students often find Ross's magnificent work too hard to follow. (Of course, some will say that it is a bad book, and that the professor can't teach...)
Even though 'A First Course...' is rarely used as a textbook (bad marketing?) after taking courses on multivariable calculus and basic probability, an undergraduate student is ready to read this book. Measure theory is barely used, and it is a surprise to see how far can one go using only probabilistic intuition. The book is also well suited to doctoral courses.
The consecutive chapters on Martingales and Brownian Motion are unparalleled, a unique collection of basic examples is used to illustrate results on Stopping Times and Convergence. Also, Measure Theory is introduced at this point in a very appealing manner. These concepts are then used to obtain classical results on Brownian Motion and other topics. Students interested in Stochastic Calculus (not covered in this book) and its many application in Finances, Engineering, Operations Research and Computer Science can acquire solid foundations here.
The chapter on Stationary Processes is also very special, it provides solid foundations for Econometrics and Time Series and it is often quoted in research papers.
In short: an excellent book to acquire solid foundations on Stochastic Processes, the only source I know for a simple and systematic introduction of certain topics.

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The purpose, level, and style of this new edition conform to the tenets set forth in the original preface. The authors continue with their tack of developingsimultaneously theory and applications, intertwined so that they refurbish and elucidate each other.The authors have made three main kinds of changes. First, they have enlarged on the topics treated in the first edition. Second, they have added many exercises and problems at the end of each chapter. Third, and most important, they have supplied, in new chapters, broad introductory discussions of several classes of stochastic processes not dealt with in the first edition, notably martingales, renewal and fluctuation phenomena associated with random sums, stationary stochastic processes, and diffusion theory.

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Dynamic Models in Biology Review

Dynamic Models in Biology
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This is an excellent book for students or faculty interested in learning more about the current state of the art in modeling of biological systems. The authors make a great effort to keep the mathematical sophistication at a level that students (or faculty) who primarily have a biological background will still be able to follow in some detail. They are also able to suggest some of the exciting current areas of research and new areas for the future. All in all, well worth reading if you are interested in the topic of modeling of biological systems.

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Fundamentals of Geographical Information Systems Review

Fundamentals of Geographical Information Systems
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I really enjoyed this book that I used in conjunction with a university summer course. I thought it was very well written and informative. The only drawback is that it is not written for any specific GIS software (e.g. ArcGIS) so it has to be very general and unbiased in its explanation of concepts.

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Artificial General Intelligence (Cognitive Technologies) Review

Artificial General Intelligence (Cognitive Technologies)
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If you are interested in human-level artificial intelligence you probably should own this book. I liked reading the book and am glad I own it but there are criticisms. Most of the book is too qualitative. Even where prototype software has been deployed algorithms are not given, even in pseudocode. Too much of the book is speculation. I also think that too little attention has been paid to the control of complexity.

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Pattern Recognition, Third Edition Review

Pattern Recognition, Third Edition
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I bought this book to teach my students on the subject. I am a professor in computer engineering and PR was not my research focus. However, there are many topics covered in this book, which have become more applicable in our area of research (VLSI design). We found this book easy to use. The algorithms are clearly described and my students could implement them easily by just reading the specific chapters we need. We think this is an excellent book to teach ourselves how to apply various PR algorithms in our domain.

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The Lady Anatomist: The Life and Work of Anna Morandi Manzolini Review

The Lady Anatomist: The Life and Work of Anna Morandi Manzolini
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In the gothic thriller _The Mysteries of Udolpho_ (1794), the mysteries consist of distinguishing the real from the supernatural, and one of the scary visions seen by the heroine Emily was a body in grave clothes, being eaten by worms. She really saw it, and the author reflects, "On such an object, it will be readily believed, that no person could endure to look twice." Is it a horrific supernatural vision, or is it a mere waxwork? If you didn't look twice, you could not tell, because waxworks were of a high degree of artistry at the time. In the eighteenth century, Anna Morandi Manzolini made waxworks not to scare people but to educate them in anatomy. Her spectacular creations were in demand in palaces all over Europe, and while she got some acclaim for her work at the time, her position as a woman without formal education meant she did not get all the recognition she deserved. Though she amplified and corrected the work of more famous anatomists of the time, like Valsalva and Malpighi, her name is not in the anatomy hall of fame today. _The Lady Anatomist: The Life and Work of Anna Morandi Manzolini_ (University of Chicago Press) by Rebecca Messbarger offers an appreciation of this remarkable woman, and gives lots of lovely pictures of the anatomical waxworks that made her famous.
Bologna, Italy, was in the eighteenth century a center for anatomical studies. There Anna Morandi was born in 1714. We know almost nothing of her education; how she learned to use Latin or to write scientific treatises with exactitude is a mystery. It is only upon her marriage at twenty six years old to Giovanni Manzolini that she comes into view. Anna Morandi began as an assistant to her husband, and became his equal. When died unexpectedly she took over the business. Wax models were an efficient way to teach anatomy. They did not rot or stink or convey disease, and lasted for centuries. The parts of a model could be laid out in the best way to distinguish them and their relationships. This was the sort of model that the husband and wife team made. It was a household business; there was still a stigma of dissecting the dead, and so the teaching of practical anatomy and surgery was not done in the university itself. The couple had a well-regarded anatomy school, and hundreds of cadavers would come into the home for their research. We don't know, but this must have made for bizarre domesticity, as such quotidian activities as child-rearing and cooking had to go on, too. Those making the Grand Tour would stop in for anatomical demonstrations, and she taught anatomy classes to those pursuing a medical career and to amateurs who just wanted the best offered in this branch of science. Anna Morandi was famous, and she received commissions from royalty such as Catherine the Great. Bologna valued her as showing how a woman might be part of the local renaissance and enlightenment; indeed, there was a tradition of "learned women" within Bologna. However, she still had much prejudice to overcome, some of it surprising to our way of thinking. For instance, anyone who regards her career can tell that she was part of the anatomical scientific effort, but her contemporaries would have regarded her at the lower level of artisan, providing her exceptional instruments to science but not being a scientist herself. She had financial struggles after the death of her husband, and was denied by the elite of Bologna a fair stipend for her services to the community or a position in an educational institution.
Two and a half centuries after her death, her vibrant models can be found in many collections, but most shown in the amazing photographs here are at the University of Bologna. Anna Morandi could not find a position at the university, but her sculptures remain, strange and detailed and beautiful, illustrating the complex cosmos that all of us carry about with us every day without thinking. Anna Morandi had a new way to bring to light those hidden regions; Messbarger's handsome book brings to light a previously hidden scientific personality.


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A New Kind of Science Review

A New Kind of Science
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This review took almost one year. Unlike many previous referees (rank them by Amazon.com's "most helpful" feature) I read all 1197 pages including notes. Just to make sure I won't miss the odd novel insight hidden among a million trivial platitudes.
On page 27 Wolfram explains "probably the single most surprising discovery I have ever made:" a simple program can produce output that seems irregular and complex.
This has been known for six decades. Every computer science (CS) student knows the dovetailer, a very simple 2 line program that systematically lists and executes all possible programs for a universal computersuch as a Turing machine (TM). It computes all computable patterns, including all those in Wolfram's book, embodies the well-known limits of computability, and is basis of uncountable CS exercises.
Wolfram does know (page 1119) Minsky's very simple universal TMs from the 1960s. Using extensive simulations, he finds a slightly simpler one. New science? Small addition to old science. On page 675 we find a particularly simple cellular automaton (CA) and Matthew Cook's universality proof(?). This might be the most interesting chapter. It reflects that today's PCs are more powerful systematic searchers for simple rules than those of 40 years ago. No new paradigm though.
Was Wolfram at least first to view programs as potential explanations of everything? Nope. That was Zuse. Wolfram mentions him in exactly one line (page 1026): "Konrad Zuse suggested that [the universe] could be a continuous CA." This is totally misleading. Zuse's 1967 paper suggested the universe is DISCRETELY computable, possibly on a DISCRETE CA just like Wolfram's. Wolfram's causal networks (CA's with variable toplogy, chapter 9) will run on any universal CA a la Ulam & von Neumann & Conway & Zuse. Page 715 explains Wolfram's "key unifying idea" of the "principle of computational equivalence:" all processes can be viewed as computations. Well, that's exactly what Zuse wrote 3 decades ago.
Chapter 9 (2nd law of thermodynamics) elaborates (without reference)on Zuse's old insight that entropy cannot really increase in deterministically computed systems, although it often SEEMS to increase. Wolfram extends Zuse's work by a tiny margin, using today's more powerful computers to perform experiments as suggested in Zuse's 1969 book. I find it embarassing how Wolfram tries to suggest it was him who shifted a paradigm, not the legendary Zuse.
Some reviews cite Wolfram's previous reputation as a physicist and software entrepreneur, giving him the benefit of the doubt instead of immediately dismissing him as just another plagiator. Zuse's reputation is in a different league though: He built world's very first general purpose computers (1935-1941), while Wolfram is just one of many creators of useful software (Mathematica). Remarkably, in his history of computing (page 1107) Wolfram appears to try to diminuish Zuse's contributions by only mentioning Aiken's later 1944 machine.
On page 465 ff (and 505 ff on multiway systems) Wolfram asks whether there is a simple program that computes the universe. Here he sounds like Schmidhuber in his 1997 paper "A Computer Scientist's View of Life, the Universe, and Everything." Schmidhuber applied the above-mentioned simple dovetailer to all computable universes. His widely known writings come out on top when you google for "computable universes" etc, so Wolfram must have known them too, for he read an "immense number of articles books and web sites" (page xii) and executed "more than a hundred thousand mouse miles" (page xiv). He endorses Schmidhuber's "no-CA-but-TM approach" (page 486, no reference) but not his suggestion of using Levin's asymptotically optimal program searcher (1973) to find our universe's code.
On page 469 we are told that the simplest program for the data is the most probable one. No mention of the very science based on this ancient principle: Solomonoff's inductive inference theory (1960-1978); recent optimality results by Merhav & Feder & Hutter. Following Schmidhuber's "algorithmic theories of everything" (2000), short world-explaining programs are necessarily more likely, provided the world is sampled from a limit-computable prior distribution. Compare Li & Vitanyi's excellent 1997 textbook on Kolmogorov complexity.
On page 628 ff we find a lot of words on human thinking and short programs. As if this was novel! Wolfram seems totally unaware of Hutter's optimal universal rational agents (2001) based on simple programs a la Solomonoff & Kolmogorov & Levin & Chaitin. Wolfram suggests his simple programs will contribute to fine arts (page 11), neither mentioning existing, widely used, very short, fractal-based programs for computing realistic images of mountains and plants, nor the only existing art form explicitly based on simple programs: Schmidhuber's low-complexity art.
Wolfram talks a lot about reversible CAs but little about Edward Fredkin & Tom Toffoli who pioneered this field. He ignores Wheeler's "it from bit," Tegmark & Greenspan & Petrov & Marchal's papers, Moravec & Kurzweil's somewhat related books, and Greg Egan's fun SF on CA-based universes (Permutation City, 1995).
When the book came out some non-expert journalists hyped it without knowing its contents. Then cognoscenti had a look at it and recognized it as a rehash of old ideas, plus pretty pictures. And the reviews got worse and worse. As far as I can judge, positive reviews were written only by people without basic CS education and little knowledge of CS history. Some biologists and even a few physicists initially were impressed because to them it really seemed new. Maybe Wolfram's switch from physics to CS explains why he believes his thoughts are radical, not just reinventions of the wheel.
But he does know Goedel and Zuse and Turing. He must see that his own work is minor in comparison. Why does he desparately try to convince us otherwise? When I read Wolfram's first praise of the originality of his own ideas I just had to laugh. The tenth time was annoying. The hundredth time was boring. And that was my final feeling when I laid down this extremely repetitive book:exhaustion and boredom. In hindsight I know I could have saved my time. But at least I can warn others.

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Atmospheric Modeling, Data Assimilation and Predictability Review

Atmospheric Modeling, Data Assimilation and Predictability
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I had this book for courses in numerical weather prediction and in data assimilation (DA), and was far from impressed.
While the historical overview in Chapter 1 is a pleasant read, the litany of errors in equations begins quickly in Chapter 2. Terms as simple as in the equations of motion are incorrect. Some things are unforgiveable in a textbook, and an error in a sophomore-level equation is one of them.
I can manage the mess of linear algebra in the text. However, Kalnay's writing style is more of one suited for lecture notes; she jumps around from topic to topic, and provides very little transition in some spots. Other locations in the notes that need further explanation (and probably would get it, if students could stop the author and ask questions), are left too vague. This is not the way to write a very good textbook, and I do not believe this book is anywhere near "very good."
I give it two stars, however, because it's the most current book in the field; there aren't many other places to turn for discussions on Kalman filters, etc. That said, if you are looking for a practical discussion of NWP or DA, or how to implement a DA scheme, you'll have a difficult time weeding it out of this book.

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This comprehensive text and reference work on numerical weather prediction covers for the first time, not only methods for numerical modeling, but also the important related areas of data assimilation and predictability. It incorporates all aspects of environmental computer modeling including an historical overview of the subject, equations of motion and their approximations, a modern and clear description of numerical methods, and the determination of initial conditions using weather observations (an important new science known as data assimilation).

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Mathematics in Nature: Modeling Patterns in the Natural World Review

Mathematics in Nature: Modeling Patterns in the Natural World
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For those of us who admire nature and see it as a product of processes both beautiful and rational, Adam's book is the perfect bedside long-termer for anyone more than casually interested in math or the intricate patterns in nature.
This book is chock full of ponderous examples of mathematical simplicity and complexity in nature, and reading it I was constantly reading only one topic and then putting the book down for days to think about and tinker with the question myself.
Good pictures, solid math (I prefer clean, modelistic equations to numerical approximations anyday), and a charming, conversational writing style make this book highly readable and highly inspiring in the way it makes you reexamine your perception of nature as unintegrated or inelegant. The very repetition of mathematical themes throughout nature - such as the omnipresent Golden Ration - proves otherwise.
For me, this is staying on my "constantly referenced" shelf.

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Methods in Neuronal Modeling - 2nd Edition: From Ions to Networks (Computational Neuroscience) Review

Methods in Neuronal Modeling - 2nd Edition: From Ions to Networks (Computational Neuroscience)
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Great book for the theorist and experimentalist! I used the section on Epilepsy and the Neural Code for a grant I wrote. This book is a great reference and time spent reading it is very well rewarded. I bought the 1st & 2nd editions which are very different. Both editions are worth buying if one is involved with computer modeling, computation, mathematics, and plain old fashion recording neurophysiology.

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Much research focuses on the question of how information is processed innervous systems, from the level of individual ionic channels to large-scale neuronalnetworks, and from "simple" animals such as sea slugs and flies to cats andprimates. New interdisciplinary methodologies combine a bottom-up experimentalmethodology with the more top-down-driven computational and modeling approach. Thisbook serves as a handbook of computational methods and techniques for modeling thefunctional properties of single and groups of nerve cells.The contributors highlightseveral key trends: (1) the tightening link between analytical/numerical models andthe associated experimental data, (2) the broadening of modeling methods, at boththe subcellular level and the level of large neuronal networks that incorporate realbiophysical properties of neurons as well as the statistical properties of spiketrains, and (3) the organization of the data gained by physical emulation of thenervous system components through the use of very large scale circuit integration(VLSI) technology.The field of neuroscience has grown dramatically since the firstedition of this book was published nine years ago. Half of the chapters of thesecond edition are completely new; the remaining ones have all been thoroughlyrevised. Many chapters provide an opportunity for interactive tutorials andsimulation programs. They can be accessed via Christof Koch's Website.Contributors :Larry F. Abbott, Paul R. Adams, Hagai Agmon-Snir, James M. Bower, Robert E. Burke,Erik de Schutter, Alain Destexhe, Rodney Douglas, Bard Ermentrout, FabrizioGabbiani, David Hansel, Michael Hines, Christof Koch, Misha Mahowald, Zachary F.Mainen, Eve Marder, Michael V. Mascagni, Alexander D. Protopapas, Wilfrid Rall, JohnRinzel, Idan Segev, Terrence J. Sejnowski, Shihab Shamma, Arthur S. Sherman, PaulSmolen, Haim Sompolinsky, Michael Vanier, Walter M. Yamada.

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Introduction to Graphical Modelling Review

Introduction to Graphical Modelling
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Because graphic methods are very popular in statistics, when you read the title you might think this is a book on the use of graphics in statistics. That is not what the book is about. The directed graph on the cover might be a hint for some.
The book deals with the theory of undirected and directed graphs which has applications to causal modeling in statistics and the development of expert systems (which Edwards claim are now more commonly referred to as probabilistic networks).
This subject is being made popular again based on the recent work of Edwards, Pearl, Rubin and a few others. The book incorporate the approach in many classical statistical problems. This is not commonly seen except in specialized texts on latent variable models.
Edwards discusses implementation of the methods with the freeware MIMS that is available in Denmark and on the web. The book is very well written and applications in MIMS are given throughout the text. Edwards also provides us with an excellent list of references (over 200 with many on causal modeling).
The software LISREL produced by researchers in the US at UCLA for latent variable and path analyses is only briefly mentioned on page 217. The lack of coverage of American and British publications on this topic is the only drawback I see.


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A useful introduction to this topic for both students and researchers, with an emphasis on applications and practicalities rather than on a formal development. It is based on the popular software package for graphical modelling, MIM, freely available for downloading from the Internet. Following a description of some of the basic ideas of graphical modelling, subsequent chapters describe particular families of models, including log-linear models, Gaussian models, and models for mixed discrete and continuous variables. Further chapters cover hypothesis testing and model selection. Chapters 7 and 8 are new to this second edition and describe the use of directed, chain, and other graphs, complete with a summary of recent work on causal inference.

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Two-Sided Matching: A Study in Game-Theoretic Modeling and Analysis (Econometric Society Monographs) Review

Two-Sided Matching: A Study in Game-Theoretic Modeling and Analysis (Econometric Society Monographs)
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This book is totally dedicated to matching theory. The book in itself is rather general, it doesn't focus on any particular application, and discusses matching as a general abstract theory. Therefore the book can be used by both micro- and macro people equally. It basically covers the problem of one-to-one and one-to-many matchings and tries to explain when equilibriums exist and if matches are unique or non-unique and comes with algorithms or constructive methods to actually do the matching. The structure of the book is mostly like math books and at each step presents an algorithm or a theorem or lemma that states a result. In most cases the proof comes afterwards. However this doesn't undermine the practicality of the book as you can easily locate the algorithm or theorem that embodies your required result and just use it. In my opinion this is one of those books you want to keep on your shelf and refer to every now and then when need be.

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Two-sided matching provides a model of search processes such as those between firms and workers in labor markets or between buyers and sellers in auctions. This book gives a comprehensive account of recent results concerning the game-theoretic analysis of two-sided matching. The focus of the book is on the stability of outcomes, on the incentives that different rules of organization give to agents, and on the constraints that these incentives impose on the ways such markets can be organized. The results for this wide range of related models and matching situations help clarify which conclusions depend on particular modeling assumptions and market conditions, and which are robust over a wide range of conditions.

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Organic Chemistry with Learning by Modeling CD-ROM Review

Organic Chemistry with Learning by Modeling CD-ROM
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The Pros: Whew... This coupeled with a great teacher for the course have made Organic Chemistry a little less painfull than it would otherwise be.
An extremely easy read compared to my Chemistry I and II texts, Carey doesn't just spew chemical jargon like so many Chemistry authors tend to do. He provides unique visuals, supporting facts, and side notes that provide usefull information, and occasional real world application stories about the importance of organic chemistry... like in the 60s Dr.s used the wrong enantiomer (certain shape) of a chemical that had a mirror image chemical that should have been used instead... this lead to birth defects in childeren...It takes what your are learning and shows you how this is and has been important.
Another Pro: The student solution manual comes with complete answers and detailed explanations to all questions... a big help when I get stuck on a 'but why'... problem.
The con: The included CD-ROM for Molecular modeling isn't that good. A really hard-to-use program, definately not user friendly, and I'm an advanced pc user. If you have about a month before your Organic class begins, you could probably go through the Chapter summaries at the end of the text, and the programs online help site to figure it out, but once you start Organic I, you're not going to have time to waste figuring this out... I suggest the classic plastic molecular model kits (available here on amazon). The pretince hall ball and stick models seem to be the Chem norm and I like them the best.

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From its first edition through this, its sixth,Organic Chemistry has been designed to meet the needs of the "mainstream," two-semester, undergraduate organic chemistry course. This best-selling text gives students a solid understanding of organic chemistry by stressing how fundamental reaction mechanisms function and how reactions occur. With the addition of handwritten solutions, new cutting-edge molecular illustrations, updated spectroscopy coverage, seamless integration of molecular modeling exercises, and state-of-the-art multimedia tools, the 6th edition of Organic Chemistry clearly offers the most up-to-date approach to the study of organic chemistry.

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