Showing posts with label mathematics. Show all posts
Showing posts with label mathematics. 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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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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Concepts and Applications of Finite Element Analysis, 4th Edition Review

Concepts and Applications of Finite Element Analysis, 4th Edition
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I am a graduate student of solid mechanics and I have read quite a few books on FEA. Of all the textbooks I have read-this is clearly the worst. The authors don't spend any time to make the material coherent and organized. They seem to have published this book just for the sake of establishing their names in the field. It is basically a collection of research papers on the subject. The worst part of the book is that the authors use excessive verbiage to describe extremely inportant concepts with little or no mathematics. This leaves the readers confused and disoriented. This book is not for those looking for an introductory text and is useless to even those experienced in the field. Avoid this book.

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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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Financial Modelling with Jump Processes (Chapman & Hall/CRC Financial Mathematics Series) Review

Financial Modelling with Jump Processes (Chapman and Hall/CRC Financial Mathematics Series)
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A book dealing comprehensively with discontinuous asset prices has long been overdue. This is a first attempt to fill the gap in a manner both rigorous and accessible. The reason why it has taken so long for a book of this kind to appear is that price jumps give rise to a host of issues that are simply not present in continuous models such as Black-Scholes. The authors tackle most of them admirably. The book also contains valuable comprehensive bibliography.
Every pioneer can make a mistake. The authors do not shy away from very complicated questions, such as (locally) optimal hedging in the presence of jumps. I'm afraid they haven't done their homework properly in this case. They claim on page 339 "the minimal martingale measure preserves orthogonality", which happens to be true for continuous price processes but it is false in most models with jumps. Pages 340 and 341 go on to compute the locally risk minimizing hedging coefficients based on the false premise. I hope this can be fixed in the next edition.


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WINNER of a Riskbook.com Best of 2004 Book Award!During the last decade, financial models based on jump processes have acquired increasing popularity in risk management and option pricing. Much has been published on the subject, but the technical nature of most papers makes them difficult for nonspecialists to understand, and the mathematical tools required for applications can be intimidating. Potential users often get the impression that jump and Lévy processes are beyond their reach.Financial Modelling with Jump Processes shows that this is not so. It provides a self-contained overview of the theoretical, numerical, and empirical aspects involved in using jump processes in financial modelling, and it does so in terms within the grasp of nonspecialists. The introduction of new mathematical tools is motivated by their use in the modelling process, and precise mathematical statements ofresults are accompanied by intuitive explanations. Topics covered in this book include: jump-diffusion models, Lévy processes, stochastic calculus for jump processes, pricing and hedging in incomplete markets, implied volatility smiles, time-inhomogeneous jump processes and stochastic volatility models with jumps. The authors illustrate the mathematical concepts with many numerical and empirical examples and provide the details of numerical implementation of pricing and calibration algorithms. This book demonstrates that the concepts and tools necessary for understanding and implementing models with jumps can be moreintuitive that those involved in the Black Scholes and diffusion models. If you have even a basic familiarity with quantitative methods in finance, Financial Modelling with Jump Processes will give you a valuable new set of tools for modelling market fluctuations.

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Automatic Control Systems Review

Automatic Control Systems
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I had to use this book for my last semester in electrical engineering. It is really interesting, well explained and complete for a concise introduction about control engineering and design of controllers.
All you need to follow the text are basic notions in engineer's mathematics (a bit of calculus and algebra). A few chapters offer reviews of important notions (transfert functions, state variable modeling, differential equations, ...) Then, you get to the essential, control theory.
The book is well divided. First chapters cover mathematical notions you have to master in order to succeed in the next part (signal flow diagrams, block diagrams, a bit of mechanic). Then, a few chapters introduce control loops, poles and zeros interpretation (explained very well with a lot of graphs examples). A single chapter covers root loci drawing. Great explanations about controllers are complete and easy to understand. The last chapter is kept for design only with A LOT of examples for PID, phase-lag, filters, etc.
Emphasis is made on computer design with examples all along the book. The CD included offers a few useful tools to use for design with Matlab.
The author knows his subject more than anyone else. He has good experience in design and the text is well written. A must for anyone studying the subject.

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Automatic Control Systems provides engineers with a fresh new controls book that places special emphasis on mechatronics. It follows a revolutionary approach by actually including a physical lab. In addition, readers will find authoritative coverage of modern design tools and examples. Current mechatronics applications build motivation to learn the material. Extensive use of virtual lab software is also integrated throughout the chapters. Engineers will gain a strong understand of control systems with the help of modern examples and exercises.

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Computing the Electrical Activity in the Heart (Monographs in Computational Science and Engineering) Review

Computing the Electrical Activity in the Heart (Monographs in Computational Science and Engineering)
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For a cross-disciplinary mathematical biologist who is to be an end-user of cardiac models, this book gave a quick introduction to electrocardiograms, electromechanics, and pertinent computing algorithms. It provides worked examples which should be useful in conjunction with a dedicated course book e.g. on partial differential equations. Regrettably, many terms and abbreviations used in the text are absent in the index, detracting from the book's value as a quick reference. The illustrations could be much better, considering the multidimensional, multivariate nature of the system. Instead, they suffer from unlabeled axes, nonuniform color maps in multi-panel figures, or wasteful use of color plates where different linestyles would suffice. All in all, not a feast for the eye but a reasonably concise, fairly well-structured primer on a specific sub-area of a huge research field.

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This book describes mathematical models and numerical techniques for simulating the electrical activity in the heart. It gives an introduction to the most important models, followed by a detailed description of numerical techniques. Particular focus is on efficient numerical methods for large scale simulations on both scalar and parallel computers. The results presented in the book will be of particular interest to researchers in bioengineering and computational biology.

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Modelling Fixed Income Securities and Interest Rate Options (2nd Edition) Review

Modelling Fixed Income Securities and Interest Rate Options (2nd Edition)
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This book is a must for any financial engineer interested in learning the HMJ model of interest-rate instruments. The HMJ model is an arbitrage model based on the instaneous forward rates. The book starts with a brief introduction to fixed-income securities followed by a rigorous treatment of binomial trees. Claim replication is then addressed through trading strategies. The instruments treated are coupon bonds,forward,futures,options and exotics. I found useful to derive the mathematical statements as I was reading the book to acquaint myself with the notation and the mathematical concepts. The beauty of the model is worth the effort. It would have been nice to include a more thorough treatment of mortage-backed securities and derivatives subject to default. Other models ( Ho-Lee , Hull-White , Vasicek ) are also briely mentioned. The parameter estimation also deserved more space since a correct estimation is more important to pricing than a clever choice of the model. To conclude: a recommended introduction to HJM + additional readings will allow the financial engineer to grasp the fundamentals of the fixed-income universe.

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This book teaches the basics of fixed-income securities in a way that, unlike competitive texts, requires a minimum of prerequisites.While other books focus heavily on institutional details of the bond market, all of which could easily be learned "on the job," Jarrow is more concerned with presenting a coherent theoretical framework for understanding all basic models.His unified approach-the Heath Jarrow Morton model-under which all other models are presented as special cases, enhances understanding while avoiding repetition.The author's pricing model is widely used in today's securities industry. In this revised edition, the author has added new chapters to enrich coverage, and has modified the order of chapters slightly to smooth the progression of material from simple to complex.Online material will be available with the text, replacing the diskette included in the first edition; lecture notes for instructors will be available on PowerPoint slides.MathWorks has provided a free online, limited version of the MATLAB's financial derivatives toolbox, with which users of the book can apply the theory presented in each chapter.

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Mathematics and Technology (Springer Undergraduate Texts in Mathematics and Technology) Review

Mathematics and Technology (Springer Undergraduate Texts in Mathematics and Technology)
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A interesting book covering a wide range of applications. The titles of the first four, of eleven, chapters gives a sense of the broad diversity of topics: (1) Positioning on Earth and in Space, (2) Friezes and Mosaics, (3) Robotic motion, (4) Skeletons and Gamma-Ray Radiosurgery. The material is generally well-written and is always interesting and instructive. This translation contains the occasional awkward sentence and, at least for U.S. readers, the occasional variant spellings, e.g., surprizing instead of surprising. At times, these can interrupt the smooth 'flow' of the text. Translation issues aside, this is a book that is both understandable and worth understanding.
Debatably, the most interesting application area presented is "Friezes and Mosaics", with its connection to linear algebra, symmetries, and transformations. Not surprisingly, applications discussed here are generally not unique to this work, and also appear in other application collections, e.g, the first chapter of "The Lighter Side of Mathematics" edited by Richard Guy also contains a discussion of frieze patterns.
There is some issue with marketing's description of the necessary mathematical prerequisites. Its overly optimistic to say, this book is "suitable for any curious individual with a decent command of high school math". This is an under-specification of the full prerequisite requirements.
For example, in the second chapter on Friezes and Mosaics readers are asked to remember, from their prior course work, "the classification of extrema of two variables using the second partial derivative test" and "the Hessian matrix". To gain full value from all chapters, readers will need in addition to linear algebra and Euclidean geometry, basic probability theory, as well as single variable and multivariable calculus. That is, they'll need more mathematical experience and maturity than might be implied from the specified prerequisite of a " decent command of high school math".
In conclusion: Acknowledging the occasional, albeit minor, awkwardness of the translation, for those with the appropriate mathematical prerequisites, this text can be recommended for its informative presentation of a variety of diverse and interesting mathematical applications.

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Multivariate Statistical Modelling Based on Generalized Linear Models (Springer Series in Statistics) Review

Multivariate Statistical Modelling Based on Generalized Linear Models (Springer Series in Statistics)
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Back in 2000 Stephen Fienberg gave a talk at the University of California at Irvine on the 2000 census and his book "Who Counts". After the talk I went to dinner with him, my colleague Bob Newcomb and Anita Iannucci. Driving to dinner Bob ask Steve for a recommendation on a multivariate textbook. A number of choice were mentioned. Bob's favorite was Cooley and Lohnes but that was a bit dated. He was definitely looking for an applied text and not a theoretical one. I learned my multivariate analysis out of the first edition of Ted Anderson's book. But that is traditional multivariate Gaussian theory and is not at all an applied text. I always liked Gnanadesikan's book and I mentioned that. Srivastava and carter is an applied text that I like and there are many other choices.
I don't recall many of Fienberg's suggestions but I do distinctly recall that he did say that now you can teach it as a special case of the generalized linear models. The idea seemed to make sense to me but I couldn't picture the details. This book is apparently the book Fienberg had in mind. He might have been thinking about the first edition because this second edition was not out then.
The book is very applied and modern and covers many important topics for biostatisticians. Coverage includes multicategorical responses, semi and nonparametric modelling, time series and longitudinal data, random effects models, state space models including Kalman Filters and nonlinear models, and survival analysis. This is not traditional multivariate data but covers many type of multivariate data and models that do not fit the standard multivariate Gaussian theory.
Chapter 4 on selecting and checking models seems to deal with the classical linear models taking a non-standard approach through the methods of generalized linear models.
Excellent text for an applied course and for a reference book. It also covers hidden Markov models and Bayesian methods (including the MCMC implementation and the WinBugs software).


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The book is aimed at applied statisticians, graduate students of statistics, and students and researchers with a strong interest in statistics and data analysis. This second edition is extensively revised, especially those sections relating with Bayesian concepts.

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Optimal Estimation of Dynamic Systems, Second Edition (Chapman & Hall/CRC Applied Mathematics & Nonlinear Science) Review

Optimal Estimation of Dynamic Systems, Second Edition (Chapman and Hall/CRC Applied Mathematics and Nonlinear Science)
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It presents the fundamentals of state estimation theory and the tools for the design of state-of-the-art algorithms for navigation and tracking, vehicle attitude determination. There is a lot of material that is covered by this book. The examples are well presented and they really help you when working on the problems at the end of each chapter. Also, computer routines for all the examples shown in the text can be accessed. I have to say that this is an excellent book for estimation of dynamic systems.

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Optimal Estimation of Dynamic Systems, Second Edition highlights the importance of both physical and numerical modeling in solving dynamics-based estimation problems found in engineering systems. Accessible to engineering students, applied mathematicians, and practicing engineers, the text presents the central concepts and methods of optimal estimation theory and applies the methods to problems with varying degrees of analytical and numerical difficulty. Different approaches are often compared to show their absolute and relative utility. The authors also offer prototype algorithms to stimulate the development and proper use of efficient computer programs. MATLAB codes for the examples are available on the book's website.New to the Second EditionWith more than 100 pages of new material, this reorganized edition expands upon the best-selling original to include comprehensive developments and updates. It incorporates new theoretical results, an entirely new chapter on advanced sequential state estimation, and additional examples and exercises. An ideal self-study guide for practicing engineers as well as senior undergraduate and beginning graduate students, the book introduces the fundamentals of estimation and helps newcomers to understand the relationships between the estimation and modeling of dynamical systems. It also illustrates the application of the theory to real-world situations, such as spacecraft attitude determination, GPS navigation, orbit determination, and aircraft tracking.

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Information Theory, Inference and Learning Algorithms Review

Information Theory, Inference and Learning Algorithms
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Uniting information theory and inference in an interactive and entertaining way, this book has been a constant source of inspiration, intuition and insight for me. It is packed full of stuff - its contents appear to grow the more I look - but the layering of the material means the abundance of topics does not confuse.
This is _not_ just a book for the experts. However, you will need to think and interact when reading it. That is, after all, how you learn, and the book helps and guides you in this with many puzzles and problems.

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Information theory and inference, often taught separately, are here united in one entertaining textbook. These topics lie at the heart of many exciting areas of contemporary science and engineering - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics, and cryptography.This textbook introduces theory in tandem with applications. Information theory is taught alongside practical communication systems, such as arithmetic coding for data compression and sparse-graph codes for error-correction. A toolbox of inference techniques, including message-passing algorithms, Monte Carlo methods, and variational approximations, are developed alongside applications of these tools to clustering, convolutional codes, independent component analysis, and neural networks.The final part of the book describes the state of the art in error-correcting codes, including low-density parity-check codes, turbo codes, and digital fountain codes -- the twenty-first century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, David MacKay's groundbreaking book is ideal for self-learning and for undergraduate or graduate courses. Interludes on crosswords, evolution, and sex provide entertainment along the way.In sum, this is a textbook on information, communication, and coding for a new generation of students, and an unparalleled entry point into these subjects for professionals in areas as diverse as computational biology, financial engineering, and machine learning.

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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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A First Course in Differential Equations (Undergraduate Texts in Mathematics) Review

A First Course in Differential Equations (Undergraduate Texts in Mathematics)
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Yeah, it's an ok book, but it's got a lot of typos and doesn't follow the curriculum of many Dif Equ classes. The first chapter is packed with tons of real applications and whatnot to distract you from actually getting to solve some Dif Equ's. By the time you get to chapter 4 it's pretty basic though, still doesn't cover everything in other classes (and covers some stuff not in other classes.

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This concise and up-to-date textbook is designed for the standard sophomore course in differential equations. It treats the basic ideas, models, and solution methods in a user friendly format that is accessible to engineers, scientists, economists, and mathematics majors. It emphasizes analytical, graphical, and numerical techniques, and it provides the tools needed by students to continue to the next level in applying the methods to more advanced problems. There is a strong connection to applications with motivations in mechanics and heat transfer, circuits, biology, economics, chemical reactors, and other areas. Moreover, the text contains a new, elementary chapter on systems of differential equations, both linear and nonlinear, that introduces key ideas without matrix analysis. Two subsequent chapters treat systems in a more formal way. Briefly, the topics include: First-order equations: separable, linear, autonomous, and bifurcation phenomena; Second-order linear homogeneous and non-homogeneous equations; Laplace transforms; and Linear and nonlinear systems, and phase plane properties.

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Mathematics: Modeling Our World Course 4 Pre-Calculus Review

Mathematics: Modeling Our World Course 4 Pre-Calculus
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If you are a math teacher looking for activities using the graphing calculator and trying to engage your students, I recommend this book. It has ideas you can center your teaching around, or you can slavishly follow the book. Forensics and rockets - who could say no? The graphing calculator instructions are all included, there are extra materials on the web site.

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The Finite Element Method and Applications in Engineering Using ANSYS® Review

The Finite Element Method and Applications in Engineering Using ANSYS®
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This book is far superior to any other ANSYS FE book. It has something like 40 examples and the cd includes the batch input files. Other books on the subject (see Moaeveni) lack the # of example problems or batch file processing tutorials. Great for beginers and intermediate users who want to get the most out of ANSYS.

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This user-friendly book provides the reader with a theoretical and practical knowledge of the finite element method (FEM) and with the skills required to analyze engineering problems with ANSYS. A self-contained, introductory text, it minimizes the need for additional reference material, covering the fundamental topics in FEM as well as advanced topics concerning modeling and analysis with ANSYS. Extensive examples from various engineering disciplines are presented in a step-by-step fashion, focusing on the use of ANSYS through both the Graphics User Interface (GUI) and the ANSYS Parametric Design Language (APDL). It includes a CD-ROM with the "input" files for the example problems.

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Stochastic Partial Differential Equations : A Modeling, White Noise Functional Approach (Probability and Its Applications) Review

Stochastic Partial Differential Equations : A Modeling, White Noise Functional Approach (Probability and Its Applications)
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SUMMARY: This book presents a new approach to stochastic partial differential equations based on white noise analysis. The framework makes heavy use of functional analysis and its main starting point is the Wiener chaos expansion and analogous expansions on different functional spaces (Schwartz spaces).
A stochastic PDE is a PDE containing a random noise term, which may be additive or multiplicative. One of the problems when working with Stochastic PDEs is to define a notion of solution which is meaningfully extendable to the nonlinear case. Problems arises because the noise term is highly irregular: for each sample of the noise, one has a (nonlinear) PDE with a very irregular term in it. In physical terms, one may encounter "ultraviolet" divergences. So, one is first faced with an existence/ unicity problem for such equations. Additionally, one would like to describe probabilitic properties of such solutions.
The method proposed by the authors can be described as follows: first, one expands the noise term in the PDE using a Wiener chaos expansion. Truncating the expansion at a certain order n yields a "regularized" equation in which the noise is smoothened. This can be roughly described as an ultraviolet cutoff. The equation then has a unique solution in an appropriate functional space. The solution of the original SPDE is then defined as the sequence of truncated solutions. In some cases, this sequence may converge in some classical sense in an appropriate function space to a weak or strong solution defined in the usual sense. But, in general, this is not the case and the notion of solution defined by the authors may be different from classical notions.
Although the title contains the word 'modeling', it may look as the abstract definition of solution proposed by the authors may have little to do with the physical notion of solution. One feels a need for a justification why this definition of a solution is physically relevant at all, which I feel is lacking. The authors give some examples, such as the noisy Burgers equation and the Kardar-Parisi-Zhang equation, but the results predicted for the solutions seem to be different than the ones predicted for example by renormalization group analysis for example regarding the scaling exponents for KPZ. Also, it would be interesting to compare this notion of solution with more classical ones for example using the semigroup/ Green function approach.
The approach proposed bears a strong resemblance to ultraviolet regularization schemes used in renormalization group theory. In fact, this framework may be seenas a probabilistic setting for renormalization methods.Unfortunately there is little discussion of this point in the book.
The first chapters contain an interesting review of white noise expansions and chaos expansions, useful in their own interest.
Overall I recommend this book as interesting for researchers in mathematical and theoretical physics.

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The main emphasis of this work is on stochastic partial differential equations. First the stochastic Poisson equation and the stochastic transport equation are discussed; then the authors go on to deal with the Schrodinger equation, the heat equation, the nonlinear Burgers' equation with a stochastic source, and the pressure equation. The white noise approach often allows for solutions given by explicit formulas in terms of expectations of certain auxiliary processes. The noise in the above examples are all of a Gaussian white noise type. In the end, the authors also show how to adapt the analysis to SPDEs involving noise of Poissonian type.

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