Molecular Modelling: Principles and Applications (2nd Edition)

Customer Reviews:

clear narrative of concepts.......2006-12-25

Leach explains how to do serious molecular modelling. Naturally, this has to be done by computer simulations. The text is a blend of the physical principles and equations needed, and how to implement as code. For example, there is the Lennard-Jones potential for intermolecular attraction. But modelling using this often also involves a cutoff. The problem with the latter is that it can create discontinuities in both the potential and the force [which is the derivate of the former]. You can shown how to suitably modify matters to avoid these complications.

Another familiar potential is the Morse potential, used to model bond stretching. Empirical but very useful, and in general quite adequate for most simulations.

The text even has an example of modelling a high temperature superconductor. Partly to enhance the relevance of the narrative for practical applications.

Comprehensive and self-contained.......2005-07-01

In this book, Andrew Leach has done a great job in putting in almost every important concept, sundry as well as significant, from the field of computational chemistry and molecular modeling. From basic but very useful concepts like atom types, Z matrices, and force field parametrization, to advanced topics like Ewald Sums and Low Mode Monte Carlo conformational searching, Leach gives due importance to everything. The discussions on quantum mechanics in the first few chapters are moderate on the mathematics without shying away from it, and provide just the right amount of detail. Later chapters cover the whole gamut of computational techniques, from Molecular Dynamics and Molecular Mechanics, to Moelcular Similarity and QSAR. Examples that are relevant in chemistry and biology are scattered throughout the book and illustrate every key idea. There are many good books for Computational Chemistry and Molecular Modeling, and some are good for a few topics, others for other ones. However, if one wants to get a grip on ALL important topics in the area, I think this is the most comprehensive reference that one can look up.

An excellent overview.......2004-06-04

Good:
This book gives an excellent overview of molecular simulation techniques starting with quantum mechanics ab initio type calculations and going up through molecular dynamics and polymer simulation. It gives a good step up from your standard physical chemistry text (such as Atkins or Chang) to being able to read the literature on modern molecular modelling techniques.

Bad:
The treatment of many methods is somewhat superficial.
The book was first written in 1996 and updated in 2000 - it is starting to get a bit out of date.

Overall I would recommend this as a solid introduction and reference.

The bible.......2003-02-12

If you could choose only one book about molecular modelling - this should be it. Everything is covered, more or less thourough, from ab initio to molecular docking, algorithms, force fields, molecular dynamics, etc. It is well written, but also works well if you want to look up single key words. The book can be read by novices to molecular modelling as well as it is useful for experts. I can highly recommend it.

Review by a molecular tyro.......2002-11-20

I'd like to recommend this book from the perspective of someone who is new to the field. I have only an informal background in chemistry and biology and an undergraduate physics degree that's 20 years old. Mr. Leach works through a broad range of material, from numeric solutions to the quantum equations for a molecule to algorithms for searching through the conformational space. His descriptions have to be concise in order to fit the enormous volume of material he has to cover, yet I found that I had no trouble following along. He often takes a historical approach. I found this effective. I would find myself wandering down blind alleys when examining the early solutions, then read the later art and have a greater appreciation for the problem than if the ultimate answer were presented first. I have been able to use much of the book as a practical guide in my work.

Book Description

Monte Carlo simulation has become an essential tool in the pricing of derivative securities and in risk management. These applications have, in turn, stimulated research into new Monte Carlo methods and renewed interest in some older techniques.

This book develops the use of Monte Carlo methods in finance and it also uses simulation as a vehicle for presenting models and ideas from financial engineering. It divides roughly into three parts. The first part develops the fundamentals of Monte Carlo methods, the foundations of derivatives pricing, and the implementation of several of the most important models used in financial engineering. The next part describes techniques for improving simulation accuracy and efficiency. The final third of the book addresses special topics: estimating price sensitivities, valuing American options, and measuring market risk and credit risk in financial portfolios.

The most important prerequisite is familiarity with the mathematical tools used to specify and analyze continuous-time models in finance, in particular the key ideas of stochastic calculus. Prior exposure to the basic principles of option pricing is useful but not essential.

The book is aimed at graduate students in financial engineering, researchers in Monte Carlo simulation, and practitioners implementing models in industry.

Mathematical Reviews, 2004: "... this book is very comprehensive, up-to-date and useful tool for those who are interested in implementing Monte Carlo methods in a financial context."

Customer Reviews:

Review for Monte Carlo Methods... by P. Glasserman.......2007-07-16

The book is just right for a reader who is looking for state-of-the-art techniques in Monte-Carlo methods in general. The fact that the book is specific to financial systems does not limit the usability of the book in the manner it is written. There are a lots of useful references one can get out of this book.
The book is for advanced readers in the sense that it requires rigorous mathematical ability to understand all the concepts. It is by no means for a novice reader and requires background in computational mathematics.

Best financial engineering book on MC.......2007-06-29

This is like the bible of Monte Carlo methods in financing. Both a good read and a good reference book. Must have! for any quant on wall street.

good book on Monte Carlo in Finance.......2007-04-02

But it seems the author is a little focused on selling his ideas, but not a very subjective overview of all topics in M-C method in finance.

Excelent choice on finance Monte Carlo.......2007-03-08

Clear and sound theoretical background on applied Monte Carlo for finance.

Brilliant.......2006-12-26

Almost everything related to Monte Carlo in Financial Engineering is covered at just the right level of detail. Quite easy to read too.

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
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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
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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.

Introduction to Numerical Methods and MATLAB: Implementations and Applications

Average customer rating:

Okay for a text
Useful Book

Introduction to Numerical Methods and MATLAB: Implementations and Applications
Gerald W. Recktenwald , and Gerald Recktenwald
Manufacturer: Prentice Hall
ProductGroup: Book
Binding: Hardcover

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ASIN: 0201308606

Book Description

From the Back Cover: The outstanding pedagogical features of this book are: o use of numerical experiments as a means of learning

why numerical methods work and how they fail; o a separate chapter reviewing the basics of applied

linear algebra, and how computations involving

matrices and vectors are naturally expressed in MATLAB; o use of a range of examples from those that provide a

succinct illustration of a basic algorithm, to those

that develop solutions to substantial problems in

engineering; o consistent use of well-documented and structured code

written in the MATLAB idiom; o a library of general purpose routines—-the NMM

Toolbox-—that are readily applied to new problems; o a progressive approach to algorithm development

leading the reader to an understanding of the more

sophisticated routines in the built-in MATLAB toolbox.

Customer Reviews:

Okay for a text.......2006-03-18

This book is okay for a text book, however there arent many examples, and the examples that are there are very simple. There are solutions for some problems online, though most of them are very basic and don't help very much.

Useful Book.......2004-08-28

This is a nice book for scientists and engineers. There are MATLAB programs already written that you can download from the webpage very easily, and modify for your specific use. I am no programmer, so having programs I can easily modify is a plus. This is meant to only be a review of linear algebra, so if you are trying to learn that subject, you will probably need to supplement this text with another book. This is not terribly in-depth on the MATLAB either. But it is a very useful handbook of plotting and interpolation methods, and how to choose the best methods for your particular set of data.

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
Binding: Hardcover

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Accessories:

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
ProductGroup: Book
Binding: Hardcover

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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.)

Average customer rating:

Outstanding introduction
A long expected book in molecular modeling is finally here
Beautifully written!
Never short of something exciting
Excellent book for both students and researchers

Molecular Modeling and Simulation
Tamar Schlick
Manufacturer: Springer
ProductGroup: Book
Binding: Hardcover

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ASIN: 038795404X

Book Description

This book evolved from an interdisciplinary graduate course entitled Molecular Modeling developed at New York University. Its primary goal is to stimulate excitement for molecular modeling research while introducing readers to the wide range of biomolecular problems being solved by computational techniques and to those computational tools. The book is intended for beginning graduate students in medical schools and scientific fields such as biology, chemistry, physics, mathematics, and computer science. Other scientists who wish to enter, or become familiar, with the field of biomolecular modeling and simulation may also benefit from the broad coverage of problems and approaches. The book surveys three broad areas: biomolecular structure and modeling: current problems and state of computations; molecular mechanics: force field origin, composition, and evaluation techniques; and simulation methods: geometry optimization, Monte Carlo, and molecular dynamics approaches. Appendices featuring homework assignments, reading lists, and other information useful for teaching molecular modeling complement the material in the main text. Extensive use of world wide web resources is encouraged, and additional course and text information may be found on a supplementary website. Some praise for Tamar Schlick¿s ¿Molecular Modeling and Simulation: An Interdisciplinary Guide¿:||"The interdisciplinary structural biology community has waited long for a book of this kind which provides an excellent introduction to molecular modeling.¿|¿Harold A. Scheraga, Cornell University||"A uniquely valuable introduction to the modeling of biomolecular structure and dynamics. A rigorous and up-to-date treatment of the foundations, enlivened by engaging anecdotes and historical notes.¿|¿J. Andrew McCammon, Howard Hughes Medical Institute, University of California at San Diego||"I am often asked by physicists, mathematicians and engineers to recommend a book that would be useful to get them started in computational molecular biology. I am also often approached by my colleagues in computational biology to recommend a solid textbook for a graduate course in the area. Tamar Schlick has written the book that I will be recommending to both groups. Tamar has done an amazing job in writing a book that is both suitably accessible for beginners, and suitably rigorous for experts.¿|¿J.J. Collins, Boston University

Customer Reviews:

Outstanding introduction.......2004-05-13

... not only to molecular modeling, but to some of the subtleties of DNA and protein behavior and geometry, too.

This book's focus is generally on interactions with large molecules, DNA and proteins, although it does discuss small molecules (drugs, a few dozen to a few hundred atoms) too. That means that it skips most of the quantum mechanical modeling of more advanced computational chemistry texts.

Nothing is lost, because Schlick covers her chosen topic (molecular modeling and dynamics) in such detail. She starts with a very clear discussion of the structure of large biomolecules, with emphasis on the features that need quantitative description for modeling. That covers protein structure at ever level. It also covers DNA/RNA structure in the best detail I've ever seen. The double-helix is the just the starting point. There are alternative helix forms, non-standard binding between nucleotides, and asymmetries caused by nucleotide composition. The next chapters describe the geometric model and, briefly, the forces acting between atoms.

The second half of the book gets down to the nuts and bolts of modeling. This includes numerical techniques, minimization, sampling and Monte Carlo techniques, and the start of dynamics. Schlick attacks some of the nasty points of the calculations, such as modeling of forces that act on very different time scales. As with the simpler material, the development is clear, descriptive, and free of pointless theorems. The meticulous reader should come away able to implement most or all of the techniques described. The level of presentation is consistent and approachable. I think freshman physics should be enough preparation for most students to get most of the value out of the discussion.

The book is written with clarity as a top priority. The glossary is in the front, making sure that the reader knows it's a first-class part of the text. After that, every chapter starts with a list of the mathematical symbols and variables used and a one-line description of each. These are small things, but they increase the book's readability immensely. The illustrations are generally informative enough. On the whole, though, they don't seem quite up to the level of the textual and mathematical presentations.

I needed a crash course in the mathematical techniques used for describing molecular structure and behavior. I should have read this book first - its clarity and thoroughness would have saved me a lot of time. After this one, I can now go back and reread the more complex texts with more hops of understanding. Do yourself a favor and read this one first.

A long expected book in molecular modeling is finally here.......2004-02-17

I highly recommend Professor T. Schlick's book. It is beautifully written with many examples and great illustrations. The book is truly interdisciplinary; it covers, in good depth, both the biological and mathematical aspects of computational structural biology. Most chapters start with an amenable introduction and finish with "hands-on" recommendations and future challenges. I was particularly pleased with the level of detail in each chapter (in particular those that show the reader the advantages and pitfalls of the different methods presented). My colleague Mariel Vazquez and I used this book in the design and preparation of our "Special topics in Mathematics" course at the UC Berkeley Mathematics Department during the Spring of 2003.

This upper-level undergraduate/lower-level graduate course was centered on mathematical and computational models of the three dimensional structure of DNA, and DNA topology. We found Professor T. Schlick's book very useful in our class preparation. In particular we covered chapter 5 (DNA structure) completely, sections 3 and 4 from chapter 7 (basic principles and formulation of atomic interactions in molecular mechanics), and several sections or subsections from chapters 8 and 9 (force terms used in molecular dynamics simulations). We also covered most of the material in chapter 10 (Multivariate Minimization), and gave a brief introduction to chapter 11 (Monte-Carlo techniques) and chapter 12 (Molecular Dynamics algorithms).

Chapter 5 starts with a very amenable and brief introduction that relates DNA with other biological processes and describes some of the challenges in studying DNA structure. It continues