Introduction to Algorithms

Amazon.com

Aimed at any serious programmer or computer science student, the new second edition of Introduction to Algorithms builds on the tradition of the original with a truly magisterial guide to the world of algorithms. Clearly presented, mathematically rigorous, and yet approachable even for the math-averse, this title sets a high standard for a textbook and reference to the best algorithms for solving a wide range of computing problems.

With sample problems and mathematical proofs demonstrating the correctness of each algorithm, this book is ideal as a textbook for classroom study, but its reach doesn't end there. The authors do a fine job of explaining each algorithm. (Reference sections on basic mathematical notation will help readers bridge the gap, but it will help to have some math background to appreciate the full achievement of this handsome hardcover volume.) Every algorithm is presented in pseudo-code, which can be implemented in any computer language, including C/C++ and Java. This ecumenical approach is one of the book's strengths. When it comes to sorting and common data structures, from basic linked lists to trees (including binary trees, red-black, and B-trees), this title really shines, with clear diagrams that show algorithms in operation. Even if you just glance over the mathematical notation here, you can definitely benefit from this text in other ways.

The book moves forward with more advanced algorithms that implement strategies for solving more complicated problems (including dynamic programming techniques, greedy algorithms, and amortized analysis). Algorithms for graphing problems (used in such real-world business problems as optimizing flight schedules or flow through pipelines) come next. In each case, the authors provide the best from current research in each topic, along with sample solutions.

This text closes with a grab bag of useful algorithms including matrix operations and linear programming, evaluating polynomials, and the well-known Fast Fourier Transformation (FFT) (useful in signal processing and engineering). Final sections on "NP-complete" problems, like the well-known traveling salesman problem, show off that while not all problems have a demonstrably final and best answer, algorithms that generate acceptable approximate solutions can still be used to generate useful, real-world answers.

Throughout this text, the authors anchor their discussion of algorithms with current examples drawn from molecular biology (like the Human Genome Project), business, and engineering. Each section ends with short discussions of related historical material, often discussing original research in each area of algorithms. On the whole, they argue successfully that algorithms are a "technology" just like hardware and software that can be used to write better software that does more, with better performance. Along with classic books on algorithms (like Donald Knuth's three-volume set, The Art of Computer Programming), this title sets a new standard for compiling the best research in algorithms. For any experienced developer, regardless of their chosen language, this text deserves a close look for extending the range and performance of real-world software. --Richard Dragan

Topics covered: Overview of algorithms (including algorithms as a technology); designing and analyzing algorithms; asymptotic notation; recurrences and recursion; probabilistic analysis and randomized algorithms; heapsort algorithms; priority queues; quicksort algorithms; linear time sorting (including radix and bucket sort); medians and order statistics (including minimum and maximum); introduction to data structures (stacks, queues, linked lists, and rooted trees); hash tables (including hash functions); binary search trees; red-black trees; augmenting data structures for custom applications; dynamic programming explained (including assembly-line scheduling, matrix-chain multiplication, and optimal binary search trees); greedy algorithms (including Huffman codes and task-scheduling problems); amortized analysis (the accounting and potential methods); advanced data structures (including B-trees, binomial and Fibonacci heaps, representing disjoint sets in data structures); graph algorithms (representing graphs, minimum spanning trees, single-source shortest paths, all-pairs shortest paths, and maximum flow algorithms); sorting networks; matrix operations; linear programming (standard and slack forms); polynomials and the Fast Fourier Transformation (FFT); number theoretic algorithms (including greatest common divisor, modular arithmetic, the Chinese remainder theorem, RSA public-key encryption, primality testing, integer factorization); string matching; computational geometry (including finding the convex hull); NP-completeness (including sample real-world NP-complete problems and their insolvability); approximation algorithms for NP-complete problems (including the traveling salesman problem); reference sections for summations and other mathematical notation, sets, relations, functions, graphs and trees, as well as counting and probability backgrounder (plus geometric and binomial distributions).

Book Description

There are books on algorithms that are rigorous but incomplete and others that cover masses of material but lack rigor. Introduction to Algorithms combines rigor and comprehensiveness.

The book covers a broad range of algorithms in depth, yet makes their design and analysis accessible to all levels of readers. Each chapter is relatively self-contained and can be used as a unit of study. The algorithms are described in English and in a pseudocode designed to be readable by anyone who has done a little programming. The explanations have been kept elementary without sacrificing depth of coverage or mathematical rigor.

The first edition became the standard reference for professionals and a widely used text in universities worldwide. The second edition features new chapters on the role of algorithms, probabilistic analysis and randomized algorithms, and linear programming, as well as extensive revisions to virtually every section of the book. In a subtle but important change, loop invariants are introduced early and used throughout the text to prove algorithm correctness. Without changing the mathematical and analytic focus, the authors have moved much of the mathematical foundations material from Part I to an appendix and have included additional motivational material at the beginning.

Customer Reviews:

Excellent buy.......2007-09-24

I bought a new copy of the book, and was happy to receive it the way I expected. Got free shipping with this one.

Confusing to say the least.......2007-09-14

This book does not provide enough examples to really get the ideas across. It is a thick read that provides little help to the subject matter unless the reader already has a wealth of knowledge on mathematical proofs and algorithms to begin with.

If the book had a solution manual, or at least explained many of the things that occur in the problem sections that never show up in the actual reading, then it would be a much easier to understand textbook.

Fantastic algorithms book.......2007-06-03

This is one of the few books that I've kept from my undergrad days as a computer science major. Although I haven't been doing software development in a while, I still use it for reference once in a while. It's easy to understand and timeless reference book. I work for a large DoD company and quite a few of my co-workers have this book on their shelves as well. (We all went to different colleges.)

Good Reference, Poor Textbook.......2007-04-19

This is a good reference for researchers, but it is not suitable for beginners. For anyone who try to study algorithms in the beginning, he just needs the big picture of this course, but this book contains too many mathematical proofs. In other words, the beginners just want a cup of milk, but the authors of this book give them a whole cow.

Although this book is quite huge, it does not contain some important topics, like online algorithms, randomized algorithms ... etc. In fact, this book should try to 'lose its weight' in order to get more useful knowledge.

The book contains a lot of interesting exercises, but does not indicate any hints or solutions. In fact, some of those exercises are too hard for students, and the authors should try to announce all sloutions in the website.

Too much and too little.......2007-03-02

+ Defacto standard
+ Accompanying WebCourse

- Too deep if used as an intro book; lacks solutions if used for a reference book
- It's HUGE!; hard to carry around

= Tries to appease too wide an audience. Definately attractive to professors who already know the information and feel this is THE book yet probably too deep for an intro algorithms class. Wish there was a searchable pdf version that came with the book on a CD as well as odd numbered solutions.

Advanced modelling in finance using Excel and VBA

Average customer rating:

Absolutely Great!
A Cookbook for Financial Modellers
Not really satisfying
Advanced modelling in finance using Excel and VBA
Highly Recommended

Advanced modelling in finance using Excel and VBA
Mary Jackson , and Mike Staunton
Manufacturer: Wiley
ProductGroup: Book
Binding: Hardcover

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ASIN: 0471499226

Book Description

This new and unique book demonstrates that Excel and VBA can play an important role in the explanation and implementation of numerical methods across finance. Advanced Modelling in Finance provides a comprehensive look at equities, options on equities and options on bonds from the early 1950s to the late 1990s.

The book adopts a step-by-step approach to understanding the more sophisticated aspects of Excel macros and VBA programming, showing how these programming techniques can be used to model and manipulate financial data, as applied to equities, bonds and options. The book is essential for financial practitioners who need to develop their financial modelling skill sets as there is an increase in the need to analyse and develop ever more complex 'what if' scenarios.

Specifically applies Excel and VBA to the financial markets
Packaged with a CD containing the software from the examples throughout the book

Note: CD-ROM/DVD and other supplementary materials are not included as part of eBook file.

Customer Reviews:

Absolutely Great!.......2006-01-11

Advanced Modeling is a fantastic book, and pretty easy to follow with relatively few coding errors. There are some books out there that have errors in the code that they use, which makes it difficult or someone to learn the techniques. Even for those without a solid background in VBA, this book can benefit you to start learning how to code properly. Anyone who is relatively proficient with Excel can definitely gain a new trick or two from reading this book. All you really need to start using this book is a basic understanding of finance concepts (e.g. portolio theory, capital budgeting, binomial options pricing, Black-Scholes, etc.). The techniques that are taught are also useful in other modeling exercises, and not necessarily just for finance-related topics.

A Cookbook for Financial Modellers.......2005-10-19

Yes, the book won't teach you CAPM, Black-Scholes, or much financial theory. But there is NO shortage of those books. There is a shortage of books with real-world Excel solutions to applying financial theory to data. I've had this book for a couple of years and have probably only used 10% of it, only because I don't have time, real business need, to do the rest. I sometimes take it to bed to read and dream of having the time to try out some of their other models. That's the only thing I can add to the other reviews here, the amount of love and passion for the subject put into this book. There's not one extra padded word or graphic in this book. Yes, if there was one book I'd have to take to a desert island with Excel and some financial data this would be it.

Not really satisfying.......2004-06-05

One of the main points of programming books is to help the reader understand the models being programmed. On this count, "Advanced modelling in finance using Excel and VBA" fails miserably. There is very little explanation of the financial concepts and models. Anyone hoping to learn finance from this book will be very disappointed.

The result is a series of programming black boxes and ugly spreadsheets having only limited usefulness.

Although the level of his book is somewhat lower, Benninga's "Financial Modeling" book is much better at explaining the conceptual basis of financial models. A good programmer will be better off with Benninga than with Jackson-Staunton.

Advanced modelling in finance using Excel and VBA.......2004-03-15

This is probably the best book written on financial modeling in excel, definitely worth the $50. Comes with a great CD-ROM. The books strength is its illustration of financial models and implantation in Excel. Since the models focus on static solutions the book is probably of greater use in academics than in industry. It would be great if there was instruction about how to input real time data into Excel and implement the models dynamically. Of particular interest to me is the great VBA code given on the CD, namely the code to calculate autocorrelation, cubic spines, eigenvalues and eigenvectors. This alone was worth the 50 bucks.

There are some major deficiencies in this book. Noticeably absent topics include: bond portfolio immunization; swap pricing; forwards and futures hedging; the ARCH, GARCH and CHARMA models.

My background is in finance, mathematics and computer science. Unlike the guy above, I don't see any need for advanced mathematics in order to study this book. In fact I am sure you don't. The point is to make excel do it for you. However it will a lot easier for those who understand the finance and mathematics behind what they are telling excel to do. I am assuming that those who are considering this book most likely have taken at least one college level calculus course and one statistics course. But I don't think even that is necessary and definitely not stochastic calculus.

Highly Recommended.......2003-03-06

VBA is one of those tools I long knew I should be proficient in but never got around to learning. That is, not until I found this book. It makes it easy for a financial professional to quickly come up to speed and start coding VBA within spreadsheets. The fact that the focus is on financial applications means that you learn coding techniques that will be useful on the job. I highly recommend the book!

The Mathematics of Financial Derivatives: A Student Introduction

Average customer rating:

Good Buy
Okay but not an introduction
Introduction to partial differential equations in finance
A good introduction to the PDE approach
waste of time

The Mathematics of Financial Derivatives: A Student Introduction
Paul Wilmott , Sam Howison , and Jeff Dewynne
Manufacturer: Cambridge University Press
ProductGroup: Book
Binding: Paperback

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ASIN: 0521497892

Book Description

Finance is one of the fastest growing areas in the modern banking and corporate world. This, together with the sophistication of modern financial products, provides a rapidly growing impetus for new mathematical models and modern mathematical methods. Indeed, the area is an expanding source for novel and relevant "real-world" mathematics. In this book, the authors describe the modeling of financial derivative products from an applied mathematician's viewpoint, from modeling to analysis to elementary computation. The authors present a unified approach to modeling derivative products as partial differential equations, using numerical solutions where appropriate. The authors assume some mathematical background, but provide clear explanations for material beyond elementary calculus, probability, and algebra. This volume will become the standard introduction for advanced undergraduate students to this exciting new field.

Customer Reviews:

Good Buy.......2007-08-29

maps one to one with many chapters in Hull. more elaborate derivations than Hull. Fixed income area treatment is very slim though. Good Buy for the Price.

Okay but not an introduction.......2006-07-31

If you want an introduction, read another book like Hull. If you want to learn how to apply Partial Differential Equations (PDEs) approach to finance then it is a useful book. However, it is better to read an elementary PDEs book before reading this book. At least, learn how to solve parabolic PDEs analytically because the technical notes in the book would not help much.

Introduction to partial differential equations in finance.......2005-10-13

This book treats only the partial differential equations
in Finance and how to treat them using Finite Differences
and Tree. For this purpose it is very well written and
understandable. A very good beginning for student. Even
undergraduate.

Now after reading it you should understand the martingales reading the baxter and how to implement Monte Carlo using, for example Glasserman (see my reviews)

A good introduction to the PDE approach.......2005-10-10

Contrary to what many readers believe, this book explains the pricing of derivatives much better than Hull. Hull gives an overview of the mechanics and properties of the derivative pricing industry, along with its pricing methodologies, and this book provides an in depth method to one of the pricing methods.

Financial derivatives can be priced by a wide range of methodologies, among some the elegant equivalent martingale measure approach (or risk-neutral pricing), replication, multinomial tree approximation, Monte Carlo simulation, partial differential equations etc etc.

This book gives an excellent introduction, and an insight to the PDE approach. Although being a big fan of the Girsanov-change-of-measure method myself, these analytical methods often fail in the valuation of highly complex derivatives like the exotics. Pricing americans prove to be hard and inefficient too, even with simulation and the risk-neutral approach.

This is where PDE methods come in. Since most derivatives (or term structures) have a PDE describing its evolution, solving the PDE seems to be a good (or sometimes the best) way, no matter how complex the derivative can get. PDEs on the other hand, have very robust and easy methods for solving. Therefore, this book brings the reader through basic PDE solving methods, analytical solutions, techniques for fast and efficient numerical approximations as well as rigorous technical explanations for some of the mathematics of partial differential equations (which arise in the financial industry).

The authors are famous for their research in the field of Industrial and Applied Mathematics, and this book continues to be a classic for undergraduates in mathematics in Oxford. If you want to have an overview of the pde approach to option valuation, without the hassle of learning up Radon-Nikodým and martingales, I highly recommend this book!

waste of time.......2005-03-10

This book is very bad, lacks almost everything you can think of, but if you don't know any better you probably won't care. It certainly needs to be supplemented by a respectable book if you want to learn derivatives (c.f. Hull's textbook, for example), and on the other hand, the math isn't rigorous at all, so you'll need a book on stochastic calculus (e.g. Michael Steele's, actually there are tons of better books out there, it's not hard to find better).

Understanding Molecular Simulation (Computational Science Series, Vol 1)

Average customer rating:

great book for MD basics
Old fashioned fortran, strong bias on Monte Carlo
Excellent text for beginners in simulation
Perfect for New Grad Students
A nice disappointment

Understanding Molecular Simulation (Computational Science Series, Vol 1)
Daan Frenkel , and B. Smit
Manufacturer: Academic Press
ProductGroup: Book
Binding: Hardcover

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ASIN: 0122673514

Book Description

Understanding Molecular Simulation: From Algorithms to Applications explains the physics behind the "recipes" of molecular simulation for materials science. Computer simulators are continuously confronted with questions concerning the choice of a particular technique for a given application. A wide variety of tools exist, so the choice of technique requires a good understanding of the basic principles. More importantly, such understanding may greatly improve the efficiency of a simulation program. The implementation of simulation methods is illustrated in pseudocodes and their practical use in the case studies used in the text.

Since the first edition only five years ago, the simulation world has changed significantly -- current techniques have matured and new ones have appeared. This new edition deals with these new developments; in particular, there are sections on:

· Transition path sampling and diffusive barrier crossing to simulaterare events
· Dissipative particle dynamic as a course-grained simulation technique
· Novel schemes to compute the long-ranged forces
· Hamiltonian and non-Hamiltonian dynamics in the context constant-temperature and constant-pressure molecular dynamics simulations
· Multiple-time step algorithms as an alternative for constraints
· Defects in solids
· The pruned-enriched Rosenbluth sampling, recoil-growth, and concerted rotations for complex molecules
· Parallel tempering for glassy Hamiltonians

Examples are included that highlight current applications and the codes of case studies are available on the World Wide Web. Several new examples have been added since the first edition to illustrate recent applications. Questions are included in this new edition. No prior knowledge of computer simulation is assumed.

Customer Reviews:

great book for MD basics.......2007-05-07

I was especially delighted about the Monte Carlo methods and the free energy calculation techniques.

Old fashioned fortran, strong bias on Monte Carlo.......2006-06-19

There is a very strong bias to MC methods in the book. What they have to say about Molecular Dynamics methods is not really new, most of it is virtually copied from the classic by Allan/Tildesley, and many MD techniques which they consider "advanced" (such as cell list methods, verlet tables, etc.) are shifted to one of the many appendices. They do not talk about ghostparticles for instance or give a detailed account of parallelized algorithms which is really state-of-the art today.
The code examples for download for the exercises, contain subtle errors, are not optimized for performance (which is THE most important thing in simulation business) and worst of all, are written in Fortran. The fact that they publish Fortran code must reflect the fact that at the time they learned how to program a computer there was no C, C++, JAVA, etc. and no object orientation in sight. Nowadays, probably no expert in programming would start a scientific and readable code in fortran. Also their definition of an algorithm is simply technically wrong. The authors are very sloppy here, have obviously no training in theoretical computer science and are obviously no experts for writing optimal code.
Scientifically, as far as physics is concerned, the book is sound, they give good arguments pro and against certain methods, but when you have already worked with Allan/Tildesley or Rappaport for many years you have the eery impression that they simply repeat many arguments from these books or from other research articles (They keep citing Allan/Tildesley a lot) Those things that are not more or less copied from other sources seems to reflect their own experience in this field which seems to be strongly limited to MC methods.
Although this book is sometimes praised I cannot really recommend it. Allan/Tildesley, and in particular the book by Rappaport are superior in stlye and in particluar as code examples are concerned. With Rappaport you get working code right away in proper C (albeit in Fortran-Style C -- again, the reason for this being the fact, that all these authors of Simulation books learned programming probably in the late 70's when Fortran was state-of-the-art). I nevertheless would recommend Rappaports book instead. The authors even offer scientific workshops based on their book (and probably make a lot of money with that). One can only hope that those are better than the coding examples of the exercises. Therefore only 2 stars.

Excellent text for beginners in simulation.......2004-11-20

Its an excellent book for those who are just beginners in MC & MD simulations. everything is very clearly explained with lot of examples and some related unsolved problems. the text explores this topic indetails with advanced chapters in later sections. Good for anybody int hsi field be it in materials science, physics or related fields.

Perfect for New Grad Students.......2002-11-24

This book is how I bootstrapped my way into being a molecular simulationist. Anyone who can program in some language can get started writing simple routines for the basic MD and MC simulations.

I do Monte Carlo simulations at Princeton, and found this book to be the most helpful available for getting my research started. It is my most common reference, and is used extensively in writing background information for various research documents.

However, after you have written your first few codes, you will pass the level of this book and need to move on. I use it less now than I did my first year.

Every student in my group (Panagiotopoulos) has this book I think. And like me, they started with it, but moved on.

A nice disappointment.......2001-08-30

The title of the book is overly ambitious and falls short on its promises. The book is a good introduction to Molecular Mechanics (MM), Molecular Dynamics (MD) and Monte Carlo (MC) methods, with detailed descriptions of the methods used and FORTRAN (pseudo)code, covering from the basics to some middle-level and some advanced algorithms.
But it does NOT cover all the fields of Molecular Modelling, just the three mentioned (MM, MD and MC), there's no coverage of quantum mechanics methods, nor QSAR or other technologies. And, while it described the algorithms, I can't think of it going all the way through up to building applications. For this, Rapaport's makes a better job, and for a general intro to Molecular Modelling, Grant & Richards' Computational Chemistry is more comprehensive (albeit at a more superficial level). Nor does it provide much detail on the methods used in modelling biological macromolecules, an increasing application field for the methods discussed in the book.
All in all, this book fails to satisfy its cover title, it won't introduce to the whole field (just the areas of MM, MD and MC) nor does it go up to application level. But it IS a REAL GOOD introduction to the subjects covered and their basic algorithms,
with sample code, detailed descriptions and plenty of references to specialized articles, texts and resources.

Useless Arithmetic: Why Environmental Scientists Can't Predict the Future

Average customer rating:

"Nature is written in the language of mathematics" (Galileo)
It's About Models
Boring
Great Idea - if only they had taken their own advice
A pivotal work - outstanding

Useless Arithmetic: Why Environmental Scientists Can't Predict the Future
Orrin H. Pilkey , and Linda Pilkey-Jarvis
Manufacturer: Columbia University Press
ProductGroup: Book
Binding: Hardcover

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ASIN: 0231132123

Book Description

Noted coastal geologist Orrin Pilkey and environmental scientist Linda Pilkey-Jarvis show that the quantitative mathematical models policy makers and government administrators use to form environmental policies are seriously flawed. Based on unrealistic and sometimes false assumptions, these models often yield answers that support unwise policies.

Writing for the general, nonmathematician reader and using examples from throughout the environmental sciences, Pilkey and Pilkey-Jarvis show how unquestioned faith in mathematical models can blind us to the hard data and sound judgment of experienced scientific fieldwork. They begin with a riveting account of the extinction of the North Atlantic cod on the Grand Banks of Canada. Next they engage in a general discussion of the limitations of many models across a broad array of crucial environmental subjects.

The book offers fascinating case studies depicting how the seductiveness of quantitative models has led to unmanageable nuclear waste disposal practices, poisoned mining sites, unjustifiable faith in predicted sea level rise rates, bad predictions of future shoreline erosion rates, overoptimistic cost estimates of artificial beaches, and a host of other thorny problems. The authors demonstrate how many modelers have been reckless, employing fudge factors to assure "correct" answers and caring little if their models actually worked.

A timely and urgent book written in an engaging style, Useless Arithmetic evaluates the assumptions behind models, the nature of the field data, and the dialogue between modelers and their "customers."

Customer Reviews:

"Nature is written in the language of mathematics" (Galileo).......2007-08-12

I picked this book up because the premise is very interesting, and one of the book jacket reviewers--an academic who is known in the modelling world--called it "a must-read for anyone serious interested in the role of models in ... science and policy."

I was very disappointed. I think critiques of modelling are useful and instructive, whether or not you believe in the approach or not (though few scientists believe it is really useless). But the critiques should be both sound and constructive, and this book provides neither.

Math is a language, for sure, but it is the least ambiguous language we humans have, and is the easiest means by which we can understand complex phenomena. I agree with the authors that qualitative knowledge is essential in science, but I think their premise fails by not more closely evaluating the postive aspects of modelling.

One may find probably the best critique of ecological modelling in Charles Hall's classic 1988 paper, "An assessment of several of the historically most influential theoretical models used in ecology and of the data provided in their support." (One may find it readily on the web.) Instead of getting this book, just read Hall's paper--you'll be better off on both counts.

It's About Models.......2007-07-26

The first author is a retired professor of geology and a particular expert on beaches. He's a scientist's scientist, and clearly an opinionated and occasionally irascible guy. This book is a bit of a tirade in places but it's full of real examples, good data, and thought provoking stories. I enjoyed it a lot. The main theme is that the natural world is too complicated a place for quantitative models to work well, and that when politics is involved they can lead to really bad decisions. The majority of examples are drawn from cases where earth sciences meet human activities - sea level rise, beach erosion and "nourishment", hydrology of abandoned pit mines, storage of nuclear waste. Closely related are discussions of fishery management and invasive species. For the most part the book is well researched. The writing is clear - the book is an easy read and never boring.
Quantitative models are decried throughout the book, and the suggestion is made that what is reasonable is "qualitative" modelling. The distinction isn't really developed until the last chapter where some good examples are to be found. Still, the distinction isn't as crisp as I'd like - perhaps it is a qualitative difference and not a quantitative one! Another positive suggestion is that incrementalism is a generally better approach to interacting with the complexities of nature than the brittle approaches that arise from an overly numerate engineering mentality. In other words, instead of using quantitative models to plan enormous, long-term projects, try something on a small scale, observe the results, and go from there.
I came away with considerably more knowledge of the topics discussed. I was already a convert to the basic themes - that we tend to overestimate what we know, to trust numbers more than we should, that political processes often interact with science in ways that are inimical to both good decisions and greater knowledge. Several times I thought of Eisenhower's dictum that plans are generally useless but planning is essential. Perhaps that captures best the distinction Pilkey is trying to make about qualitative models.
Unlike some of the other reviewers, I was not offended by the political implications of anything Pilkey asserts. I didn't see it as either pro or anti global warming in any political sense. No hidden agendas here, it's really about modelling. Recommended.

Boring.......2007-06-28

Some of the complaints in other reviews are sound, but I will mention just one. This is a dull book. Longwinded, preachy. And aside from some jargon, there isn't much substance here beyond what you could say in 20 pages.

Great Idea - if only they had taken their own advice.......2007-05-17

As a systems engineer, I have practical experience in creating, testing, critiquing, and evaluating models that attempt to explain, predict, or illustrate system processes. Any engineer learns early on that regardless of what the model says - Reality Always Wins. Thus I was very interested in this book because of its evident intent to discuss the limitations of modeling as applied to natural processes.

Unfortunately, the authors exhibit a level of bias against any model they don't approve that is so over the top that I was constantly wondering what cheese would be served with the "whine". And then they cap it off by blindly accepting an entire range of dire global warming predictions, which are entirely derived from - you guessed it - models of complex natural processes. I guess if you like the model's answers then it is magically a good model.

I have a hard time accepting what appears to be intellectual dishonesty, so although the book makes some good points, I really can't recommend it. The authors also appear to be particularly upset with certain individuals and organizations in the coastal engineering community, because the animus comes through loud and clear.

If you really want a good book on the limitations of mathematical modeling as applied to the real world, there is a two-volume set called "Reality Rules" that is much better. However, the Reality Rules books are not aimed at the layperson, so be prepared for some real math in these books.

A pivotal work - outstanding.......2007-05-13

A pivotal work. Wherever one stands on the debate over human caused global warming, this book will raise questions. A well done investigation of mathematic global modeling pitfalls.

Martingale Methods in Financial Modelling (Stochastic Modelling and Applied Probability)

Average customer rating:

Excellent introductory book to financial math
At the Forefront of Modern Mathematical Finance
Martingales & Finance
yes, but ...
excellent book for post-John-Hull readers

Martingale Methods in Financial Modelling (Stochastic Modelling and Applied Probability)
Marek Musiela , and Marek Rutkowski
Manufacturer: Springer
ProductGroup: Book
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ASIN: 3540209662

Book Description

In the 2nd edition some sections of Part I are omitted for better readability, and a brand new chapter is devoted to volatility risk. As a consequence, hedging of plain-vanilla options and valuation of exotic options are no longer limited to the Black-Scholes framework with constant volatility.

The theme of stochastic volatility also reappears systematically in the second part of the book, which has been revised fundamentally, presenting much more detailed analyses of the various interest-rate models available: the authors' perspective throughout is that the choice of a model should be based on the reality of how a particular sector of the financial market functions, never neglecting to examine liquid primary and derivative assets and identifying the sources of trading risk associated. This long-awaited new edition of an outstandingly successful, well-established book, concentrating on the most pertinent and widely accepted modelling approaches, provides the reader with a text focused on practical rather than theoretical aspects of financial modelling.

Customer Reviews:

Excellent introductory book to financial math.......2006-11-03

This book takes you through the math of finance step-by-step, passing through very simple examples first and then slowly adding complexity to the models studied. It is written very clearly and the prerequisites to reading this book are only some basic notions of probabilities (sigma-fields, probability measures).

Sometimes, the problem with math books is that they are "dry" and contain only a succession of theorems and proofs. In this one, the authors make a point of explaining in detail how different theorems and models relate to each other, and make extensive comparisons between them so that you get a better feel for how they work in practice.

The book is primarily a math book and can be light on market specifics. Do not buy this book as a practical "howto" in derivatives trading.

At the Forefront of Modern Mathematical Finance.......2005-05-23

This advanced text provides an excellent account of the current state-of-the art of options pricing/hedging models and interest rate term structure models. The book is accessible to both advanced practitioners of mathematical finance as well as to pure researchers in the field.

The book is in written in a mathematical style and contains rigorous proofs of many results. However, the main focus of the text is to describe the frontier of knowledge in the subject. Each section contains copious references to the literature and is so current that several references are to working papers. Many sections detail open problems and other areas suitable for scholarly research.

In their second edition, the authors provide an extremely useful critique of each modeling paradigm that they investigate. They also provide evidence for their position in the form of literature references which instruct the reader as to the shortcomings/limitations of a particular model. This information should prove quite valuable to model practitioners and implementers.

The authors assume an advanced background from the field of stochastic analysis, although they do provide an appendix which summarizes key results needed from the field. For the stochastic calculus prerequisites, I recommend Rogers & Williams "Diffusions, Markov Processes and Martingales" volumes I and II. Suitable prerequisites are also covered by Karatzas and Shreve in "Brownian Motion and Stochastic Calculus" 2nd edition. A good foundation in arbitrage pricing theory is also needed. I recommend the nice treatment by Bjork in "Arbitrage Theory in Continuous Time" 2nd edition.

The book is divided into two parts. The first part deals with options pricing in equity markets. Chapter 1 sets premlinaries required for the arbitrage theoretic framework, while Chapter 2 has a very nice treatment of discrete time models and finite financial markets.

In Chapter 3, the authors develop the Black-Scholes model along with the Bachelier model using arbitrage techniques. The models are compared and used as benchmark continuous time models and form the basis for all subsequent analysis.

Chapter 4 provides a nice survey of techniques used to price/hedge options in foreign equity and currency markets. The authors assume familarity of the basic workings of foriegn markets.

Chapter 5 is a terrific chapter on valuing American-style options. The American call option is thoroughly studied and approximation techniques for the American put option are introduced. The explicit derivations of the formulas are referenced to the literature.

Chapter 6 provides an introduction to exotic options, although the authors vary their use of the term 'exotic' to meaning 'not a standard European-style or American-style' in this chapter to meaning 'no readily available liquid market' in Chapter 7. The descriptions are quite accessible and the basic properties of the options are described along with pricing formulas (assuming the Black-Scholes framework).

Chapter 7 provides as complete an accounting as I have ever seen of the generalizations of the Black-Scholes model and motivates this from the point of view of volatility surfaces. Many of the well-known models are studied in detail, such as CEV, local volatility, and mixture models. The strengths and weaknesses of each model are analyzed. The stochastic volatility models of Wiggins (via Orenstien-Uhlenbeck processes), Hull-White, and Heston are studied, as is the SABR model. The chapter wraps up with a study of the SIV models, describes how the stochastic volatility models can be obtained via limits of GARCH models and surveys Jump-diffusion processes and Levy processes.

The second part of the book is concerned with term structure models and interest rate derivatives. The authors are quite well-know for their many contributions to this study and their treatment is authoritative.

Martingales & Finance.......2003-04-12

I have used this book for two courses in my MSc degree in Financial Maths...well this book is hard to understand at first glance, but, once you are introduced with a good course on stochastic analysis and applied probability, this is an illuminating book...I particularly enjoyed the part on foreing equity derivatives and exotic derivatives.....Harmed with patience this is definitely the book by which you can effectively gain a sound a knowledge on modern mathematical finance theory....reading in conjunction with Bingham-Kiesel book, could help understanding the foundation of the subject.

yes, but ..........2000-03-17

I've been using this book on and off over the last year. At first I was very impressed with the level of detail in the mathematics, especially as it was the only book at the time focussing on risk-neutral methods and covering BGM. But I've become increasing disillusioned with it of late. It's difficult to explain, but although the whole book is written in traditional theorem-proof style, there are no real proofs! (I have a PhD in math and have done research for 10 years so I should know a little about proofs.) The only "proofs" provided are basically symbol shifting, but the heart of the math is strangely absent. This is especially strange given the Springer series in which it appears.

In short, if you want a catalogue of methods this book does the job, but if you want a deeper understanding try Lars Nielsens book.

excellent book for post-John-Hull readers.......1999-08-17

This book covers essentially everything needed for a serious financial math study. It captures the spirit of modern financial math. For people with math, physics or engineering background, when you feel comfortable woth John Hull's books, then this book is right one, and a must one.

Model Selection and Multi-Model Inference

Average customer rating:

Model Selection and Multi-Model Inference
Good, but far too prolix
One of the best introduction to AIC (Akaike's Information Criterion)!!!
authoritative and thorough treatment

Model Selection and Multi-Model Inference
Kenneth P. Burnham , and David Anderson
Manufacturer: Springer
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ASIN: 0387953647

Book Description

The second edition of this book is unique in that it focuses on methods for making formal statistical inference from all the models in an a priori set (Multi-Model Inference). A philosophy is presented for model-based data analysis and a general strategy outlined for the analysis of empirical data. The book invites increased attention on a priori science hypotheses and modeling. Kullback-Leibler Information represents a fundamental quantity in science and is Hirotugu Akaike's basis for model selection. The maximized log-likelihood function can be bias-corrected as an estimator of expected, relative Kullback-Leibler information. This leads to Akaike's Information Criterion (AIC) and various extensions. These methods are relatively simple and easy to use in practice, but based on deep statistical theory. The information theoretic approaches provide a unified and rigorous theory, an extension of likelihood theory, an important application of information theory, and are objective and practical to employ across a very wide class of empirical problems. The book presents several new ways to incorporate model selection uncertainty into parameter estimates and estimates of precision. An array of challenging examples is given to illustrate various technical issues. This is an applied book written primarily for biologists and statisticians wanting to make inferences from multiple models and is suitable as a graduate text or as a reference for professional analysts.

Customer Reviews:

Model Selection and Multi-Model Inference .......2007-03-09

Those interested in mark-recapture models definitely should have this extraordinary book.
Very complete and easy to read

Good, but far too prolix.......2005-08-24

I admire this book very much for its accessible treatment of AIC, but if were reduced in length by half, it would be twice as good. The authors cannot resist repeating themselves, usually several times, especially when giving advice of the "motherhood and apple pie" variety. Another annoying feature is that many references are given for philosophical points, yet sometimes when a useful result is given without proof, no reference is provided. For example, on page 12 an expression for maximized likelihood is given without a derivation or a reference. Inside this fat book there is a thin book crying to be let out.

One of the best introduction to AIC (Akaike's Information Criterion)!!!.......2005-08-18

AIC is one of the widely known methods in model selection and inference.
This book includes not only a basic use but also advanced issues of the information-theoretic approach.
Using this book, you will learn the application of AIC soon!

authoritative and thorough treatment.......2000-12-18

Burnham and Anderson have put together a scholarly account of the developments in model selection techniques from the information theoretic viewpoint. This is an important practical subject. As computer algorithms become more and more available for fitting models and data mining and exploratory analysis become more popular and used more by novices, problems with overfitting models will again raise their ugly heads. This has been an issue for statisticians for decades. But the problems and the art of model selection has not been commonly covered in elementary courses on statistics and regression. George Box puts proper emphasis on the iterative nature of model selection and the importance of applying the principle of parismony in many of his books. Classic texts on regression like Draper and Smith point out the pitfalls of goodness of ift measures like R-square and explain Mallows Cp and adjusted R-square. There are now also a few good books devoted to model selection including the book by McQuarrie and Tsai (that I recently reviewed for Amazon) and the Chapman and Hall monograph by A. J. Miller.

Burnham and Anderson address all these issues and provide the best coverage to date on bootstrap and cross-validation approaches. They also are careful in their historical account and in putting together some coherence to the scattered literature. They are thorough in their references to the literature. Their theme is the information theoretic measures based on the Kullback-Liebler distance measure. The breakthrough in this theory came from Akaike in the 1970s and improvements and refinement came later. The authors provide the theory, but more importantly, they provide many real examples to illustrate the problems and show how the methods work.

They also refer to the recent work in Bayesian methods. Chapter 1 is a great introduction that everyone should read. Being a fan of the bootstrap I was interested in their coverage of it in chapters 4, 5 and 6 (much of which is the authors' own work).

Because the authors work in biological fields they cover survival models as well as the standard time series and regression models where most of the emphasis has been placed on model selection in the past.

Book Description

This book illustrates the ease with which AMOS 4.0 can be used to address research questions that lend themselves to structural equation modeling (SEM). This goal is achieved by: 1) presenting a nonmathematical introduction to the basic concepts and applications of structural equation modeling; 2) demonstrating basic applications of SEM using AMOS 4.0; and 3) highlighting features of AMOS 4.0 that address important caveats related to SEM analyses.

Written in a "user-friendly" style, the author "walks" the reader through 10 SEM applications from model specification to estimation to the assessment and interpretation of the output. Each of the book's applications is accompanied by:
*a statement of the hypothesis being tested;
*a schematic representation of the model under study;
*the use and function of a wide variety of icons and pull-down menus;
*a full explanation of related AMOS Graphic input models and output files;
*a model input file based on AMOS BASIC; and
*the published reference from which each application was drawn.

Customer Reviews:

Structural Equation Modeling With AMOS: Basic Concepts, Applications, and Programming (Multivariate Applications Series).......2007-02-21

Great book, easy to read. It goes well as a companion book about SEM with a more mathematically heavy text. The author makes using AMOS easy! I've already shown my Professor something about AMOS he didn't know!

Great Resource.......2006-01-29

If you are looking for a good resource on learning how to use AMOS for structural equation modeling, this is definitely the book. It is easy to understand and well laid out. Well recommended, especially for teaching SEM to students.

wonderful if familiar w/stats + new to sem.......2001-08-07

This book is a wonderful guide to understanding a good range of basics about sem, getting models to work with Amos, and interpreting your output. You will need to be familiar with one of the stats packages that Amos is compatible with. Very much user-friendly in this complicated topic. All of the statistically-related and theory-related aspects are well-referenced, so you can find sources to reference for different aspects of sem. A great book to fill the gap between the Amos user's manual and books on sem in general. (contact Erlbaum about educ pricng.)

Financial Modeling with Crystal Ball and Excel (Wiley Finance)

Average customer rating:

goes beyond deterministic assumptions
Financial Modeling with Crystal Ball and Excel

Financial Modeling with Crystal Ball and Excel (Wiley Finance)
John Charnes
Manufacturer: Wiley
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Binding: Paperback

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ASIN: 0471779725

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Praise for
Financial Modeling with Crystal Ball(r) and Excel(r)

"Professor Charnes's book drives clarity into applied Monte Carlo analysis using examples and tools relevant to real-world finance. The book will prove useful for analysts of all levels and as a supplement to academic courses in multiple disciplines."
-Mark Odermann, Senior Financial Analyst, Microsoft

"Think you really know financial modeling? This is a must-have for power Excel users. Professor Charnes shows how to make more realistic models that result in fewer surprises. Every analyst needs this credibility booster."
-James Franklin, CEO, Decisioneering, Inc.

"This book packs a first-year MBA's worth of financial and business modeling education into a few dozen easy-to-understand examples. Crystal Ball software does the housekeeping, so readers can concentrate on the business decision. A careful reader who works the examples on a computer will master the best general-purpose technology available for working with uncertainty."
-Aaron Brown, Executive Director, Morgan Stanley, author of The Poker Face of Wall Street

"Using Crystal Ball and Excel, John Charnes takes you step by step, demonstrating a conceptual framework that turns static Excel data and financial models into true risk models. I am astonished by the clarity of the text and the hands-on, step-by-step examples using Crystal Ball and Excel; Professor Charnes is a masterful teacher, and this is an absolute gem of a book for the new generation of analyst."
-Brian Watt, Chief Operating Officer, GECC, Inc.

"Financial Modeling with Crystal Ball and Excel is a comprehensive, well-written guide to one of the most useful analysis tools available to professional risk managers and quantitative analysts. This is a must-have book for anyone using Crystal Ball, and anyone wanting an overview of basic risk management concepts."
-Paul Dietz, Manager, Quantitative Analysis, Westar Energy

"John Charnes presents an insightful exploration of techniques for analysis and understanding of risk and uncertainty in business cases. By application of real options theory and Monte Carlo simulation to planning, doors are opened to analysis of what used to be impossible, such as modeling the value today of future project choices."
-Bruce Wallace, Nortel

Customer Reviews:

goes beyond deterministic assumptions.......2007-06-24

The book is all about simulations. In financial modelling, as opposed to engineering or science. Readers from the latter 2 fields who have coded simulations will find much in common. The specific equations in the text for finance are largely different from what you've met before. But the basic treatment is essentially the same.

Typically, the text will describe some financial equation. The Crystal Ball program lets you easily generate random data as input to simulations, which it then runs.

Despite Excel in the book's title, the book is mostly about using Crystal Ball. Charnes shows how you can go well beyond a simple deterministic treatment of an income statement or balance sheet. Typically, most companies just use the deterministic approach. The danger is that this approach relies on certain assumptions. Using Crystal Ball and the book, you can test the effect of relaxing these assumptions on the balance sheet. A more robust approach to financial planning.

Financial Modeling with Crystal Ball and Excel.......2007-05-13

Acho que faltou um pouco mais de detalhes nos tópicos, porém o livro apresenta excelente modelos técnicos.

Interest Rate Modelling: Financial Engineering

Average customer rating:

Good explained yield curve fitting
Could have been very good. A new edition could earn 5 stars.
A real must !
A must-have encyclopedia on term structure modeling
Extraordinary

Interest Rate Modelling: Financial Engineering
Jessica James , and Nick Webber
Manufacturer: Wiley
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ASIN: 0471975230

Book Description

This book provides a comprehensive resource on all the main aspects of valuing and hedging interest rate products. A series of introductory chapters reviews the theoretical background, pointing out the problems in using naïve valuation and implementation techniques. There follows a full analysis of interest rate models including major categories, such as affine, HJM and market models, and in addition, lesser well known types that include Consol, random field and jump-augmented models. Implementation methods are discussed in depth including the latest developments in the use of finite difference, lattice and Monte Carlo methods and their particular application to the valuation of interest rate derivatives. Containing previously unpublished material.

Interest Rate Modelling is a key reference work both for practitioners developing and implementing models for real and for academics teaching and researching in the field.

Interest Rate Modelling is an encyclopedic treatment of interest rates and their related financial derivatives. It combines advanced theory with extensive and down-to-earth data analysis in a way which is truly unique. For practitioners, students and scholars in the field, this impressive wok will be the standard reference for years to come.", Professor Tomas Bjork, , Stockholm School of Economics#

"...an excellent book. I am particularly pleased by its breadth and range of topics...the reader is provided with an informative and readable exposition.", Dr Farshid Jamshidian, , NetAnalytic#

"I particularly like the strong emphasis on the practicalities and calibration of interest rate models. This book will be invaluable as a comprehensive reference to students, researchers, and practitioners.", Professor Francis Longstaff, , The Anderson School at UCLA#"

This is a carefully written, scholarly but fascinating presentation of the field of Interest Rate Modelling. It combines the best of two worlds: the rigour expected from finance in acamedia with the relevance expected from finance in practice. James and Webber are truly masters of their market since this book is surely a must-buy for both researchers and practitioners. If only all finance books were written with this care and attention to detail.", Dr Neil Johnson, , Clarendon Laboratory, Oxford#

"Today, interest rates are key economic instruments. This is a mammoth treatise and must surely rank as one of the most comprehensive available on the topic. Anyone interested in modelling or simulating the behaviour of interest rates, be they practitioner, economist, mathematician or new entrant to the subject, will find within a wealth of pertinent material.", Professor Peter Richmond, , Trinity College Dublin#

Customer Reviews:

Good explained yield curve fitting.......2005-08-15

List different way of yield curve fitting method, and good explain why B-spline
is good. Also term structure on general manifold is interesting, although a review don't
think it's useful.

Could have been very good. A new edition could earn 5 stars........2005-03-01

While very ambitious and containing some very good material, I think there are too many errors, typos, and gaps in this book. For example, the derivation of equation (3.20) on page 43, a not insignificant result on swap rates, is embarrasingly wrong. They make 2 fundamental errors in equations (3.15) and (3.16) and appear fortunate to arrive at (3.20) which is correct. These errors are not mere typos. Their examples related to the concept of a filtration on top of page 58 appear to be wrong. Elsewhere in the book notation is often used inconsistently and without adequate definition. There are also gaps. For example, when discussing volatility structures in Section 16.1, they use equation (16.6) (which is correct) in a number of examples, but I could not find where they actually derived that equation. (It should have been in the HJM chapter) but was not there.

I like the fact that they wanted to include a chapter on term structures from the macro-economic perspective. Unfortunately this chapter is difficult to read, provides no macroeconomic intuition and again appears to omit too many details. For example, the description of the IS-LM-Phillips model is inadequate and either should be expanded or dropped from future editions. Likewise, the description of the Sommer model is inadequate. Equation (11.3) in the statement of Sommer's Theorem would appears to be wrong at first sight. The left-hand-side of that equation is known by time t, but the right-hand-side would appear to be UNknown by time t. This apparnet contradiction can be explained but the authors never comment on such matters, often making the material more difficult to follow.

Chapter 17 on GMM and MLE methods is quite nice but again, not everything is adequately explained. The examples of Section 17.2.5, for example, seem to assume that certain variables are observable in the market place (e.g. volatility, v_t in equation 17.38) but this seems inapproriate as v_t would generally be unobservable. Indeed this is stated in Chapter 18. Again, however, James and Webber provide no clarification whatsoever of this issue, leaving the reader to wonder what exactly was done.

Some sections are also poorly motivated. For example, Section 16.4, "Processes on Manifolds", in the chapter on Principal Components Analysis is not motivated properly. While the material is quite straightforward to read (though they should define terms like diffeomorphism if they want to use them in a financial engineering text), it is not clear why you need to bring in the language of manifolds and tangency spaces etc. After all, where is this material used in the example of Section 16.4.5? It seems to me that the examples of Section 16.4 are interesting and do lead to new types of term structure models, but that this material could be presented without the jargon of manifolds. Again, I may be wrong but then I would blame the authors for not writing clearly.

A final criticism is that it seems on occasion that the authors are writing about material that they are not particularly familiar with, all for the sake of being encyclopedic. This thought crossed my mind when reading the chapter on Monte Carlo simulation. For example, with reference to equations (13.11)-(13.13) it would have taken very little to point out that Jensen's Inequality implies that some estimated security prices will be biased. Indeed the authors hint at this in the final paragraph on page 350 but do not make the point.

There are many other examples of these errors / typos / gaps in the book. And as far as I know there is no list of errata available.

I will not mention the many good things in this book as other reviewers have already done so. However, I think their praise has been excessive and feel that 3 stars is appropriate.

A real must !.......2001-12-30

As a math grad student who is interested in the term structure modelling, I found that this book is really useful! It just tells you everything about interest rate modelling,not just for the no-arbitrage modelling issue, they even have a chapter about the macroeconomic foundation for interest rate fluctuation! The math used in this book is very concise without too much measure theory twaddle,Everyone who works in this field should have a copy. It's a real must!

A must-have encyclopedia on term structure modeling.......2001-05-26

I have spent a number of years in building & implementing models for interest-rate-dependent claims, but should admit: I learned more from this book. I view it as an encyclopedia on the subject, in which the authors (never heard their names before - what a shame!) have done an excellent job on reviewing hundreds of publications. The theory of term structure modeling has been grown to a separate subject - thanks to Hull and White, Jamshidian, HJM, BGM, Hughston - among main contributors. You can find all methods in one place and in a very accesible form. For example, HJM is described better and simpler than in the author's original paper. Most models are reviewed with practical implementation in mind.

It is not a "first book" on "introduction" on the subject; it is rather a good desk reference for prepared professionals.

Extraordinary.......2001-03-17

There are plenty of books on fixed income mathematics. This one is extraordinary. It is simultaneously practical, theoretically sophisticated and a pleasure to read. The treatment of term-structure models, including HJM, is the most accessible I have seen anywhere. There is a lot of information on yield curve building. This includes both bootstrapping and more recent research in parameterised curves. There are plenty of topics that other books might label "beyond the scope of ...", but James and Webber jump right in, with meaty discussions of the Kalman filter, lattice methods of valuation and GARCH models. Despite all the theory, the authors are always in touch with practical details. They take into account stub dates, and are precise about day counts. These are obviously practitioners!

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