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Bestselling author Nassim Nicholas Taleb continues his exploration of randomness in his fascinating new book, The Black Swan, in which he examines the influence of highly improbable and unpredictable events that have massive impact. Engaging and enlightening, The Black Swan is a book that may change the way you think about the world, a book that Chris Anderson calls, "a delightful romp through history, economics, and the frailties of human nature." See Anderson's entire guest review below.
Guest Reviewer: Chris Anderson
Chris Anderson is editor-in-chief of Wired magazine and the author of The Long Tail: Why the Future of Business Is Selling Less of More.
Four hundred years ago, Francis Bacon warned that our minds are wired to deceive us. "Beware the fallacies into which undisciplined thinkers most easily fall--they are the real distorting prisms of human nature." Chief among them: "Assuming more order than exists in chaotic nature." Now consider the typical stock market report: "Today investors bid shares down out of concern over Iranian oil production." Sigh. We're still doing it.
Our brains are wired for narrative, not statistical uncertainty. And so we tell ourselves simple stories to explain complex thing we don't--and, most importantly, can't--know. The truth is that we have no idea why stock markets go up or down on any given day, and whatever reason we give is sure to be grossly simplified, if not flat out wrong.
Nassim Nicholas Taleb first made this argument in Fooled by Randomness, an engaging look at the history and reasons for our predilection for self-deception when it comes to statistics. Now, in The Black Swan: the Impact of the Highly Improbable, he focuses on that most dismal of sciences, predicting the future. Forecasting is not just at the heart of Wall Street, but it's something each of us does every time we make an insurance payment or strap on a seat belt.
The problem, Nassim explains, is that we place too much weight on the odds that past events will repeat (diligently trying to follow the path of the "millionaire next door," when unrepeatable chance is a better explanation). Instead, the really important events are rare and unpredictable. He calls them Black Swans, which is a reference to a 17th century philosophical thought experiment. In Europe all anyone had ever seen were white swans; indeed, "all swans are white" had long been used as the standard example of a scientific truth. So what was the chance of seeing a black one? Impossible to calculate, or at least they were until 1697, when explorers found Cygnus atratus in Australia.
Nassim argues that most of the really big events in our world are rare and unpredictable, and thus trying to extract generalizable stories to explain them may be emotionally satisfying, but it's practically useless. September 11th is one such example, and stock market crashes are another. Or, as he puts it, "History does not crawl, it jumps." Our assumptions grow out of the bell-curve predictability of what he calls "Mediocristan," while our world is really shaped by the wild powerlaw swings of "Extremistan."
In full disclosure, I'm a long admirer of Taleb's work and a few of my comments on drafts found their way into the book. I, too, look at the world through the powerlaw lens, and I too find that it reveals how many of our assumptions are wrong. But Taleb takes this to a new level with a delightful romp through history, economics, and the frailties of human nature. --Chris Anderson
Book Description
A black swan is a highly improbable event with three principal characteristics: It is unpredictable; it carries a massive impact; and, after the fact, we concoct an explanation that makes it appear less random, and more predictable, than it was. The astonishing success of Google was a black swan; so was 9/11. For Nassim Nicholas Taleb, black swans underlie almost everything about our world, from the rise of religions to events in our own personal lives.
Why do we not acknowledge the phenomenon of black swans until after they occur? Part of the answer, according to Taleb, is that humans are hardwired to learn specifics when they should be focused on generalities. We concentrate on things we already know and time and time again fail to take into consideration what we don’t know. We are, therefore, unable to truly estimate opportunities, too vulnerable to the impulse to simplify, narrate, and categorize, and not open enough to rewarding those who can imagine the “impossible.”
For years, Taleb has studied how we fool ourselves into thinking we know more than we actually do. We restrict our thinking to the irrelevant and inconsequential, while large events continue to surprise us and shape our world. Now, in this revelatory book, Taleb explains everything we know about what we don’t know. He offers surprisingly simple tricks for dealing with black swans and benefiting from them.
Elegant, startling, and universal in its applications The Black Swan will change the way you look at the world. Taleb is a vastly entertaining writer, with wit, irreverence, and unusual stories to tell. He has a polymathic command of subjects ranging from cognitive science to business to probability theory. The Black Swan is a landmark book–itself a black swan.
Customer Reviews:
good read, interesting arguments but a bit naive.......2007-10-09
i read both books by mr. taleb (black swan and fooled by randomness) last week. i enjoyed both, although i think he made the point about the futility of inductive reasoning more persuasively in the randomness book. both argue the same point with different emphasis. in black swan, he even undertake a bit of advising, akin to list of things to do so that you don't get harmed by black swans.
often, but not for too long, i get exasperated by his desire to grind an ax: his constant put downs on economists, mba's and journalists. i can see his point that math has a lot to teach to those ignorant of subtle complexities of probability theories, but the pompous (but quite entertaining) name calling is just not necessary. having said that, he did toned down a bit in the black swan book, so if you prefer something more spicy, read the randomness book first.
mr taleb has done a good job at presenting an interesting hypothesis, i am waiting for his scholarly works to prove he is not just exercising litearary rights to fill his pocket the lazy way.
things to watch for: when he goes over how useless econmists are, he makes broad assumptions about the neo-classical econ approach. this shows that he is rather naive of this "soft" science. one point in case, there is this concept called opportunity cost, it matters when making rational decisions. he just ignored it or perhaps was not aware of its central role in how economics study the world. simlarly, we all know that models make assumptions, some are more extreme than others. they serve a purpose, benchmarking. no one expects to find a perfectly competitive market (i think there is one close to it, the commodity market), but that is NOT the point.
in sum, highly enjoyable book. taleb is at his bests explaining intuitively statistics. does a good job in criticizing inductive reasoning, but a terrible job at justifying the put downs on economics.
The Power of What We Do Not Know.......2007-10-09
Well written, informative and just a little out of the box. Taleb veers so slowly off the beaten path so as to allow the reader can adapt. In the end, the typical reader believes he/she "knew all that all along." Nice Work. Mastery.
Foggy premise presented by an arrogant author.......2007-10-08
Really can't understand all the great reviews of this book. The author THINKS he's witty, when he's really just showing off his arrogance. Trust me, he's no Larry David....as one other reviewer seemed to think.
But the real letdown of this book is his foggy writing, which is almost always a sign of foggy thinking. And to MAKE UP one of his prime examples (the author with the unpronounceable Slavic name) and then to continue to bring up other examples using "her" is simply lazy research and writing. If his premise is so true, surely he can find real life examples to share with us. He probably intimidated his editor so much that this really annoying writing style was not challenged by the publisher. A layperson can find many better books written on the subject of randomness.
Great book .......2007-10-08
Nassim Nicholas has written a very important book. The first chapters seemed a bit slow but once he got going (or maybe I got thinking) I could not stop. Anyone making any type of business, marketing, or investment decision and relies on prediction needs to read this book. My hat is off to him as a writer and a big picture thinker.
Very thoughtful and enlightening book.......2007-10-02
Taleb has a winner here. The book brings a new slant to what really drives almost every trend. Often the stock traders and predictors of political events are not just wrong, but dead wrong. The reasons for these mistakes and others are explained in entertaining fashion in this book "The Black Swan".
Book Description
The 2nd edition of this successful book has several new features. The calibration discussion of the basic LIBOR market model has been enriched considerably, with an analysis of the impact of the swaptions interpolation technique and of the exogenous instantaneous correlation on the calibration outputs. A discussion of historical estimation of the instantaneous correlation matrix and of rank reduction has been added, and a LIBOR-model consistent swaption-volatility interpolation technique has been introduced.
The old sections devoted to the smile issue in the LIBOR market model have been enlarged into several new chapters. New sections on local-volatility dynamics, and on stochastic volatility models have been added, with a thorough treatment of the recently developed uncertain-volatility approach. Examples of calibrations to real market data are now considered.
The fast-growing interest for hybrid products has led to new chapters. A special focus here is devoted to the pricing of inflation-linked derivatives.
The three final new chapters of this second edition are devoted to credit. Since Credit Derivatives are increasingly fundamental, and since in the reduced-form modeling framework much of the technique involved is analogous to interest-rate modeling, Credit Derivatives -- mostly Credit Default Swaps (CDS), CDS Options and Constant Maturity CDS - are discussed, building on the basic short rate-models and market models introduced earlier for the default-free market. Counterparty risk in interest rate payoff valuation is also considered, motivated by the recent Basel II framework developments.
Customer Reviews:
Best book on interest rate models.......2002-12-14
This is the best book available on interest rate models. Very detailed. Much more focused and readable than Rebonato's book. More pragmatic and explicit than Musiela and Rutkowski. Not as theoretical as Hunt and Kennedy. James and Webber also looks very good, but I'm not that familiar with it. All other books have only bits and pieces on interest rates.
The best book I have read on the subject.......2002-05-06
With all the due respect to the other authors I would say that if one is interested in a good theoretical book whihc is also good on the implementation side then the book of Brigo and Mercurion is definetly the best book I have ever read on the subject.
Anyone interested in implementing the LMM/BGM/MSS model in practice is well advised to read it.
I would just say that this is certainly a must have in the field.
New stuff and nice overview: hard to beat!.......2002-01-17
In the late nineties I went through Brigo's innovative work on stochastic nonlinear filtering with differential geometry techniques. I was favorably impressed by results and style, particularly in his dissertation and in his 'geometry in present day science' very readable overview. Interesting results are found and nicely told with accurate - but not pointlessly complicated - advanced mathematics for the problems at hand, I reasoned.
I've followed a similar path from control to finance, and having worked with interest rate models, I couldn't help but order this Brigo-Mercurio book. I had high expectations 'cause these two guys are working in a bank on the real thing.
Sure enough I'm not disappointed.
1-factor models are handled with great care, a ton of formulas and recipes are given. I've never seen this kind of analysis of pricing with Gaussian 1-f models. The new upgrade of the CIR model is interesting and accurate. "CIR++" is now my favorite 1-f model. I like the treatment of lognormal 1-f models and the explanation of Monte Carlo and trees -- the flow-chart for Bermudan swaptions is crystal clear! Plots of market implied structures and volatility calibration are useful additions.
The chapter on 2-f extensions has one of the best discussions on volatility, and two tons of useful formulas/recipes. Two dimensional trees!
The HJM chapter size is OK. I agree - the useful models embedded in HJM are short rate models and market models.
Market models - these three chapters alone are worth the book. You'll find yourself nodding as you read the guided tour. They make it look easy all the time. The exposition is focused, clear, intuitive, detailed. There's also new stuff, just check the calibration discussion! Smile modeling begins with a brilliant tour and ends with Brigo-Mercurio's new approach - the mixing dynamics - deserving a whole chapter if expanded.
The detailed explanation on products is a much welcome original addition. Cross currency derivatives!
Quotes - as in Brigo's old work - are a pleasant diversion while reading. The 500 and more pages are a treat given the competitive price.
Still there's room for improvements - more "CIR2++"! Something on 3-f models. Historical estimation of the correlation matrix and low-rank optimized approximations. Expand smile modeling! More hedging. Something on structured products. Cross currency libor model. chapter 9 - other interest rate models - sounds out of place and can be suppressed for other things.
This book rings true and has useful teachings for students, academics and practitioners. Although it requires some background in stochastic calculus, it's hard to beat on the pricing front. Kudos to Brigo and Mercurio! It only harms there aren't enough books like this.
Nicely written overview of interest rate models.......2001-12-15
This recent book, written by two Italian "quants" Mercurio & Brigo, gives a nice and accessible overview of interest rate models which is a compromise between the practitioner viewpoint, expressed for ex. in Rebonato's book "Interet Rate option models"
and the theoretical viewpoint such as the one in Musiela & Rutkowski.
The authors, themselves PhDs in quantitative finance/ applied maths, wrote this book while working as quants in an Italian bank and this first hand contact with the market gave them a
practical view on the subject which markes this book very interesting.
The book contains a "rational" catalogue of models used in practice ( as opposed to models which are impossible to implement!).
In contrast with academic books on interest rate modeling which deal with HJM formulation, there is a lot of emphasis here on LIBOR and Swap market models
(BGM -Jamshidian models) which reflects the current market practice. This is a positive point since there are not many books with details on implementing and using these "market models".
Part II: Interest rate models in practice is particularly useful because it deals with implementation and calibration which, as any practitioner knows, are important and usually delicate issues.
However calibration issues are dealt with somewhat lightly, especially recent developments on modeling cap/swaption smiles
are not included here.
This book can also be used for a graduate level/PhD course on interest rate models.
There are a lot of numerical examples in the book and mathematics is kept to the necessary level while keeping the
approach both rigorous and understandable.
Overall, it is one of the best books written on the subject.
I highly recommend it to PhD students, quants and researchers interested in this field.
Well written and useful book.......2001-11-04
In my humble opinion, this is the best book on Interest Rate modeling out there. The writing style is clear and focused and the appendices are fantastic. The book is rigorous but someone with some background in Stochastic Calculus will find it easy to follow. If you need refresher, dont worry the authors have you covered, see the appendix on Stochastic Calculus. Not an introductory book. Very exciting book.
Book Description
This classic text on multiple regression is noted for its non-mathematical, applied, and data-analytic approach. Readers profit from its verbal-conceptual exposition and frequent use of examples. The applied emphasis provides clear illustrations of the principles and provides worked examples of the types of applications that are possible. Researchers learn how to specify regression models that directly address their research questions. An overview of the fundamental ideas of multiple regression and a review of bivariate correlation and regression and other elementary statistical concepts provide a strong foundation for a solid understanding of the rest of the text.
The third edition features an increased emphasis on graphics and
the use of confidence intervals and effect size measures and an accompanying CD with data for most of the numerical examples along with the computer code for SPSS, SAS, and SYSTAT.
Applied Multiple Regression serves as both a textbook for graduate students and as a reference tool for researchers in psychology, education, health sciences, communications, business, sociology, political science, anthropology, and economics. An introductory knowledge of statistics is required. Self-standing chapters minimize the need for researchers to refer to previous chapters. The book is an ideal text for courses on multiple regression and correlational methods.
Customer Reviews:
Second Grad Stats.......2007-05-06
I've adopted this text for my graduate seminar in Multiple Regression. I choose it over other texts for the topics AND because it's focus is on concepts rather than math. Now that we can carry SPSSX in our brief case, there is no need to focus on that computation.
Can't beat it.......2001-04-17
...This book is the source of all you need. It's hard going at times, but so's the subject. The book's 15 years old and remains the best guide to the analysis of correlated data. It's a reference book, one I value as much as a good dictionary. To use it as a text would be misguided unless the instruction was aimed at a sophisticated audience.
Best MRC Book Ever.......2000-03-24
I agree with the previous reviewer that there are times when the exposition in the book gets a bit intense; but c'mon! We're dealing with statistics. You gotta sweat a bit. That's when learning happens. In my opinion the book is extremely clearly written. And although you may have to re-read a few sentences a few times, the basic tools for understanding most every major aspect of MRC is embedded in the text. In sum, this was a great book that I read as a 2nd-year graduate student in psychology. Unlike the first reviewer, I turned to this text when I got confused during the course lectures!
MRC Analysis---good book overall.......1999-12-15
Cohen and Cohen's MRC analysis book is well versed and easy to understand for someone that is familiar with MRC terminology, however, for first year graduate students, the text is very equivocal. The book is lacking ample illustrations of complex problems, leaving students to rely on outside sources. Also, the book uses unfamiliar symbols that do not correspond with other MRC books, which intensifies the confusion level of the students even more.
Overall, the text is a great addition to a statistical library, and this reviewer recommends it, in spite of being a sub-par book for first year graduate students.
Book Description
The fourth edition of STATISTICS FOR SOCIAL DATA ANALYSIS continues to show students how to apply statistical methods to answer research questions in various fields. Throughout the text, the authors underscore the importance of formulating substantive hypotheses before attempting to analyze quantitative data. An important aspect of this text is its realistic, hands-on approach. Actual datasets are used in most examples, helping students understand and appreciate what goes into the research process. The book focuses on the continuous-discrete distinction in considering the level at which a variable is measured. Rather than dwelling on the four conventional levels-of-measurement distinctions, the authors discuss statistics for analyzing continuous and discrete variables separately and in combination.
Customer Reviews:
Statistics for indoctrination, philosophy for real dummies.......2005-05-20
For academic philosophers of science sociology is not a paradigm of successful science. Earlier Bohrnstedt had enforced his ersatz philosophy of social science as editor of the journal Sociological Methods and Research. Now in this book, Statistics for Social Data Analysis, Bohrnstedt, Knoke and Mee attempt to indoctrinate students in this same ersatz philosophy of science.
The authors advocate their version of Haavelmo's "structural-equation" agenda, allege a distinction between unobserved conceptual variables and observable "indicators", and pontificate criteria for identifying causality prior to statistical modeling and empirical testing.
Contrast their views with some basics of contemporary pragmatism, which prevails in professional academic philosophy taught in universities today:
1. Pragmatist definition of "theory": A theory is any universally quantified statement proposed for testing. It is never defined in terms of any particular ontology - such as subjective motivations. Thus there is no philosophical problem of relating sociological theory to empirical model, because the theory is the model and the model is the theory.
2. Pragmatist criterion for criticism: Only empirical criteria may operate in the criticism of theories. Ontological ideas including preconceived claims about causality are never valid criteria. Thus theories/models may not be rejected merely because their equation specifications do not describe motivations, i.e. do not have a mentalistic ontology.
3. Pragmatist thesis of ontological relativity: The empirically tested and currently nonfalsified theory decides ontology including any claims about causality. Thus one does not firstly know causes and then make theories, but rather the empirically tested and nonfalsified theories/models describe the ontologies of their domains including causality.
4. Pragmatist thesis of pluralism: There may be and often are multiple empirically acceptable - i.e. tested and currently nonfalsified - theories/models. Thus they all make acceptably competing or complementary causal claims, so long as they are found to be empirically acceptable - i.e. not falsified.
In her book, History of Econometric Ideas, Mary S. Morgan writes that there are two ways in which econometrics has been used: (1) discovery or theory development and (2) empirical testing. The contemporary pragmatist philosophy of science assigns statistical analysis a fundamental role in theory development as well as in theory testing. Pragmatism thus invites use of data mining and artificial-intelligence computer systems, which can create and test literally billions of hypotheses.
I believe that this book, Statistics for Social Data Analysis, leaves the reader/student ignorant of the true capability of new technologies such as mechanized statistical analysis of social data for discovery, and that its provincial philosophy of science invites a Luddite attitude toward twenty-first century social science research.
Sociologists who are unaware of contemporary academic philosophy of science will likely not find this review helpful. More importantly such sociologists will also therefore be unable to exploit to their - or their students' - advantage the enabling freedom and contributing opportunities offered by the pragmatist philosophy.
For more: Google my book, History of Twentieth-Century Philosophy of Science at my web site philsci for free downloads, and to view my other book reviews at this Amazon site.
Thomas J. Hickey, Econometrician
For students of social sciences.......2003-03-31
This book is a statistics textbook for students of social sciences, not high-end users. I read earlier edition of this book in undergraduate statistics course. In that course, only basics of statistics were instructed. In social sciences, they don't need to know A to Z of statistics for all they have to know is what the function of SPSS or SAS means and what kind of data is needed and how the data would be analyzed in the statistics packages. There is no need to derive the functions in the textbook mathematically as they do in the courses of statistics department. We should understand what the function means, not how it is derived. This book is written in this regard. Unlike orthodox statistics textbook, this book tackles only the meaning of the statistical methods. In doing so, this book illustrates the methods with various field works and SPSS exercises. This is the stance most textbook written for social scientists takes. It seems that this book succeed in achieving the goal. Explanations are succinct and examples are apposite.
But this book is not that useful when you should do real research. Most social sciences articles use more advanced methods than what this book introduces. This book is good enough to beginners, but not so to who would be real researcher. At that point, you should have read more advanced ones already. If not, you couldn't read a piece of article in the common journals.
Average customer rating:
- Advanced probability topics without measure theory
- Just unnecessary
- Another poorly written text book
- Good Introductory Textbook
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Probability and Computing: Randomized Algorithms and Probabilistic Analysis
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ASIN: 0521835402 |
Book Description
Assuming only an elementary background in discrete mathematics, this textbook is an excellent introduction to the probabilistic techniques and paradigms used in the development of probabilistic algorithms and analyses. It includes random sampling, expectations, Markov's and Chevyshev's inequalities, Chernoff bounds, balls and bins models, the probabilistic method, Markov chains, MCMC, martingales, entropy, and other topics. The book is designed to accompany a one- or two-semester course for graduate students in computer science and applied mathematics.
Customer Reviews:
Advanced probability topics without measure theory.......2007-08-18
This book is underestimated by two reviewers below. I totally do not agree with them. This book covers a wide range of topics in a very readable style. The contents in this book is complementary to the book of Motwani and Raghavan (but this book is much easier to digest).
It, without requiring any knowledge on measure theory, contains excellent introductions to many difficult topics in probability including
- concentration bounds (Chernoff, Azuma-Hoeffding, etc.)
- applications of stochastic processes such as queuing theory
- martingale (Wald's equation)
- coupling of Markov chains and their mixing times
- Shannon's source coding and noisy channel theorems
- Erdos' probabilistic method
- etc.
All of these topics are provided with excellent applications in computing.
The authors illustrate many clever tricks for proving theorems, and these tricks give insights to the readers as well.
Just unnecessary.......2007-05-17
This book, while written by two renowned computer scientists, is truly disappointing. In trying to discuss randomness and computation, this book just does a mediocre job on discussing randomized computation and also an equally poor job discussing relevant aspects of probability theory. Their approach is not novel and many of their examples can be found in other texts. If you really want to learn randomized computation, get Motwani et al's book on Randomized Algorithms. If you want to learn probability theory, get any advanced probability theory book like Spencer and Alon on the probabilistic method, one of Sheldon Ross's books, or even Grimmett and Stirzaker. Whatever you do don't get this weak hybrid of a book that will require you to get another book at some point to supplement your understanding.
Another poorly written text book.......2006-03-19
The authors must be smart guys. They obviously understand alot about this subject but make the mistake that you do too! As a result, the book is inadequate as a teaching tool.
They use only half to a third of the narrative they need to adequately explain a subject. They also like to leave out proof steps or not explain them. The problems at the end of chapters are poor as well, since the authors seem to have forgotten to teach the techniques needed to solve most them in the chapter they belong to.
I am sure to them it is intuitive.
Good Introductory Textbook.......2005-03-16
It's pretty easy to get computers to do things where the answer is yes or no, or 4 or 6, given that the inputs to the problem are known. It's much harder to get an answer to a problem where the answer is that their is a 62% chance that the answer is yes. Unfortunately, in real life it's this second class of problems that predominates.
This book is oriented to solving these kinds of real world problems. The exercises in the book are chosen from real world examples -- what we used to call story problems. This tends to give the student a better understanding of not only the mathematics and programming involved but experience in looking at problems with a view to understanding this approach to solving the problem.
This book is suitable for a one or two semester introductory class at the upper undergraduate or beginning graduate level.
Just a word about the illustration on the front of the book. At the end of the book Alice in Wonderland the queen is about to order Alice beheaded. Alice says, "You're nothing but a pack of cards." At this, the whole pack rose up into the air and came flying down around her. This illustration is by John Tenniel from the original book of 1899. A deck of flying playing cards is a good way to illustrate random and probability.
Book Description
Incorporating new and updated information, this second edition of THE bestselling text in Bayesian data analysis continues to emphasize practice over theory, describing how to conceptualize, perform, and critique statistical analyses from a Bayesian perspective. Its world-class authors provide guidance on all aspects of Bayesian data analysis and include examples of real statistical analyses, based on their own research, that demonstrate how to solve complicated problems. Changes in the new edition include: · Stronger focus on MCMC · Revision of the computational advice in Part III · New chapters on nonlinear models and decision analysis · Several additional applied examples from the authors' recent research · Additional chapters on current models for Bayesian data analysis such as nonlinear models, generalized linear mixed models, and more · Reorganization of chapters 6 and 7 on model checking and data collection Bayesian computation is currently at a stage where there are many reasonable ways to compute any given posterior distribution. However, the best approach is not always clear ahead of time. Reflecting this, the new edition offers a more pluralistic presentation, giving advice on performing computations from many perspectives while making clear the importance of being aware that there are different ways to implement any given iterative simulation computation. The new approach, additional examples, and updated information make Bayesian Data Analysis an excellent introductory text and a reference that working scientists will use throughout their professional life.
Customer Reviews:
Comprehensive, but not well-written.......2007-01-06
This book is a very comprehensive treatment of Bayesian data analysis. However, it is not well-written. I find Lancaster's book to be much more well-written and interesting to read.
Very Excellent, but non-statisticians should start elsewhere.......2006-06-05
Gelman's book is an excellent and complete introduction to Bayesian methods. It covers a number of topics not touched by other intros I've read, and focuses much more on regression and ANOVA than other texts.
There are two downsides, coming from someone in psychology. First, the book seems to hover between an introductory text and a more advanced one. The topics covered are mostly introductory, but the examples aren't always entirely easy to follow. A tighter integration with the R and Bugs code would help. Perhaps a section at the end of the chapters containing a code example for each topic would be ideal. It's not that the topics themselves are necessarily opaque, but Gelman moves too fast at times, making it hard to think in terms of notation, theory, experimental design AND code at the same time (for those of us constantly thinking about how this affects our own research).
Second, as a general rule, this book is outside the ken of most psychologists. This is unfortunate since the methods are ideal for our discipline, and since many psychologists already perceive a large barrier of entry to statistics. As a psychologist with minimal undergraduate training in stats, I would (and did) start with a standard statistics book like Casella and Berger, and then move on to a gentler introduction to Bayesian methodology, like _Bayesian Methods: A Social and Behavioral Sciences Approach_ by Jeff Gill. Also, you can barely do anything in this book with SPSS so you'll have to learn R and Bugs.
As Good As It Gets For An Intro To Bayes.......2005-10-28
Yes, it is an introduction to Bayesian methods. That means you have to have a very good understanding of classical statistics (at the level of Casella and Berger would be optimal) and then be willing to use the WinBugs program to further your knowledge. A great book.
It is a good book, but not a bible of Bayesian analysis........2005-08-31
[1] A good introductory book, but definitely not a bible of Bayesian analysis.
[2] The example-based introduction may be a try of new generation of Bayesian. Many people, especially the beginners, may like this style.
[3] Some of the authors are good at programming in BUGS, R, etc, so the part of MCMC methods seems worthy to skim through.
[4] The book is suitable for the undergraduate and the first year graduate level.
A good introductory book, but..........2005-01-26
I read the other reviews and agree with them to some extent. This is
a good introduction to applied Bayesian analysis. Lots of
good examples, illustrations and exercises.
If you are the kind of person who learns by way of examples, then
this might be the text book for you. If you are looking for the
bigger picture, then you will be lost here. There is very little in the way
of theory. Why is this the right method? What is gained theoretically
over a frequentist method? What are the theoretical properties of the
proposed approach? To a large extent these kinds of questions remain a mystery.
In terms of flexibility an applied Bayesian approach has some decided
advantages. However, in terms of theory
it's almost as if the authors want you to believe that once
you adopt the Bayesian approach then the benefits of averaging
by way of using a prior will always be the right thing to do.
You could argue that advanced questions like this are better suited for
a more advanced text book. I tend to ask more out of a book.
Book Description
Statistics is a subject of many uses and surprisingly few effective practitioners. The traditional road to statistical knowledge is blocked, for most, by a formidable wall of mathematics. The approach in An Introduction to the Bootstrap avoids that wall. It arms scientists and engineers, as well as statisticians, with the computational techniques they need to analyze and understand complicated data sets.
Customer Reviews:
It is a good introduction book.......2007-05-22
This book is designed for people who do not have have background in bootstrap. I found that it is easy to read and understand. You could follow the examples of this book and directly to the "R". Reader should read this book before going to read the "Bootstrap Methods and Their Application".
Dirty your hands and you will get a lot from this book.
introduction to bootstrap.......2006-03-24
As a physician I was looking for an introductory text on bootstrapping and comparative methods. This book was useful to me to get an impression. Some mathematical background is needed.
A great book to learn the Bootstrap method from.......2004-02-05
This is the best book to learn about the bootstrap. Clear style, no empty verbiage, good problems, excellent examples are some of the qualities that make this exposition of Bootstrap great. The math level is minimal - some basic statistics (perhaps at the level of Wackerly et al's book) - is all that's required.
Not for Engineers.......2002-08-07
This book provides a good coverage of the very useful bootstrap method. However, post-graduation as an engineer, I find that the method is neither well known nor happily accepted by engineers outside of academia. In the corporate world, bootstrapping is left up to degreed statisticians, as this is what management trusts. As a mechanical engineer, I find that simpler statistical techniques, even if they include broad assumptions, are much more widely accepted. If you are an engineer, leave this up to the statisticians. If you are a statistician, this book is an acceptable source for learning bootstrap.
Great introduction by the originator of the bootstrap.......1998-07-16
Brad Efron wrote the key paper rediscovering the bootstrap and putting it in its proper place with other resampling techniques in his famous 1979 paper in the Annals of Statistics. His work was a breakthrough that has now led to hundreds of other publications and several books on the bootstrap and more general resampling procedures by himself, his students and many other statisticians. In fact I am working on a book with goals similar to what he and Rob Tibshirani achieve in this monograph. It is a concise and accurate presentation of the bootstrap and its wide variety of applications and is very much up to the state-of-the-art in this rapidly growing area of statistics. It is written in an intuitive fashion and avoids much of the mathematics (Edgeworth expansions etc.) which are needed to provide formal proof that the bootstrap does what it is intended to do. Provides most of the important references up through 1993. For a similar treatment that is more current, see Davison and Hinkley (1997). Bootstrap Methods and their Application. Those interested in the theory and formal mathematics should consult Hall (1992). The Bootstrap and Edgeworth Expansion.
Book Description
Statistical Power Analysis is a nontechnical guide to power analysis in research planning that provides users of applied statistics with the tools they need for more effective analysis. The Second Edition includes:
* a chapter covering power analysis in set correlation and multivariate methods;
* a chapter considering effect size, psychometric reliability, and the efficacy of "qualifying" dependent variables and;
* expanded power and sample size tables for multiple regression/correlation.
Customer Reviews:
Definitive - But.......2006-08-18
Absolutely the main text but check it out from the library - you will use it approximately 10-15 times in your research life.
The Definitive Power Analysis Text.......1999-12-03
Cohen does a masterful job of taking the guesswork out of statistical power estimation. This text provides procedural guidelines for determining power for many designs, and can be quite helpful in determining proper sample sizes. Not for the casual reader, but a necessary addition to any serious researchers statistical library.
The classic statistical power reference........1999-06-29
Clearly, a must for every statistical library. This book is considered the authority on power analysis.
Average customer rating:
- very nice conceptual overview
- Not for the practitioner
- Trash
- Excellent Introduction, Sparse on Details
- A Good Introductory Survey
|
Scientific Computing
Michael T. Heath
Manufacturer: The McGraw-Hill Companies, Inc.
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Matrix Computations (Johns Hopkins Studies in Mathematical Sciences)(3rd Edition)
ASIN: 0072399104 |
Book Description
Heath 2/e, presents a broad overview of numerical methods for solving all the major problems in scientific computing, including linear and nonlinear equations, least squares, eigenvalues, optimization, interpolation, integration, ordinary and partial differential equations, fast Fourier transforms, and random number generators. The treatment is comprehensive yet concise, software-oriented yet compatible with a variety of software packages and programming languages. The book features more than 160 examples, 500 review questions, 240 exercises, and 200 computer problems. Changes for the second edition include: expanded motivational discussions and examples; formal statements of all major algorithms; expanded discussions of existence, uniqueness, and conditioning for each type of problem so that students can recognize "good" and "bad" problem formulations and understand the corresponding quality of results produced; and expanded coverage of several topics, particularly eigenvalues and constrained optimization. The book contains a wealth of material and can be used in a variety of one- or two-term courses in computer science, mathematics, or engineering. Its comprehensiveness and modern perspective, as well as the software pointers provided, also make it a highly useful reference for practicing professionals who need to solve computational problems.
Customer Reviews:
very nice conceptual overview.......2006-07-22
Wow, people seem to be really split on this book. I had Mike Heath for numerical analysis/scientific computing and he was an excellent instructor, one of the best lecturers I've ever had. (As a consequence, I have a hard time separating the book and the class, so judge accordingly.) The book is based on his lecture notes, though he added some material and didn't cover every topic in the book. Just reading the book is useful to give you an overview of the point behind different methods. The goal of the class for which this book was written is actually quite conceptual. It was to give scientists (that's me: a stats researcher who makes heavy use of numerical computation) and CS people in areas other than scientific computing a leg up. It was only a first class for people in scientific computing, the rough equivalent of intro Physics or intro Probability/Stats for people in those respective majors. However, you *won't* be prepared to "roll your own" from this book. In fact, at the beginning of the semester Heath was very careful to note that if you have the opportunity to use a library function for most numerical programming, you are nuts to roll your own. Why? Numerical algorithms are usually extremely complicated and the authors of the code often spend years developing careful expertise on them. Frequently the formulas used to elucidate a given method are NOT the ones used to implement it. You need error traps, tricks to handle ill-scaling and other special cases, etc. These are things that someone who has a one-semester, superficial understanding of a topic simply won't have. So consider the book on the goals it set: it is an overview of a field. If you want to learn more about any one topic, you have to dig deeper and consult references and other works, but this is a good place to start. For this, the book serves admirably.
Not for the practitioner.......2005-11-17
If you are interested in Scientific computing from the viewpoint of the end user that is the guy who uses the method to solve practical engineering problems then this book is lacking.
Not enough methods in this book to constitute an introductory survey of the field. Every chapter gets heavy dose mathematical treatment, apparently Heath loves his math but for the rest of us it doesnt translate into know-how. Know how to solve equations using computational techniques. Very few derivations to back his mathematical swagger, very few examples (if any) and fewer numerical schemes to solve problems. Many of the chapters receive cursory treatment such as PDE's get about 70 pages of print. Far too little to do anyone any good.
He does talk about interesting issues such as conditioning and error analysis and computer precision and memory issues but it is done from such a superficial viewpoint that one cannot use anything to improve ones code. Not recommended if you want to learn numerical methods even if you have an excellent professor to learn from. His chapter on FFT's was even more abstruse and there was hardly any methods with which to solve PDE's.
I had this for a graduate course in Numerical Methods but ended up using Hoffman's excellent book on Numerical Methods.
Trash.......2005-10-14
If you want to have a solid understanding of numerical computation, this book is definitely the last choice. Many theorems are given without any proof or even intuitions behind them in this book. Even when a proof is provided, it's often far from rigorous. The organization of chapters is the worst I have ever seen, revelant materials are scattered over several different locations rather than put together. Take the SVD for example, it is mentioned in the end of chapter 3, but reappears in chapter 4, which is very confusing. If you are new to this area, please don't read this book. It gives you many many facts without explanations, which I think is not a good way to learn new things. David S. Watkins' Fundamentals of Matrix Computations is a lot better and easier to understand. It also emcompasses many detailed treatments of various theorems. If you have bought Heath's book, don't be sad, at least it can serve as a coaster.
Excellent Introduction, Sparse on Details.......2004-11-20
While sparse on the details of many of the algorithms and theorems mentioned, as an introduction it covers a broad range of material-enough for two semesters of study. The writing is lucid, and when a proof of a theorem is given, it is easy to follow and explained in english afterward. Rationale is given for everything, which is a great benefit to a student not familiar with the nuances of sophisticated linear algebra.
A Good Introductory Survey.......2002-11-05
This book excels at presenting a reader with little to no knowledge in computer science and a mild mathematical background (knowledge of differential equations as a prerequisite) with the fundamental concepts regarding scientific computing. The presentation of pseudo-code algorithms helps smooth the transition from analytical (pencil and paper) thinking to numerical thinking. The algorithms are presented in a manner such tha anyone with access to dozens of possible environments can apply them, though they are by no means complete, thus requiring some thought into the processes. The material covered is 110% of what an engineer will want to know, 90% of what an applied mathematician will want to know, and 45% of what a numerical analyist will want to know. In all, a great book to begin a foray into numerical computing.
Average customer rating:
- On first reading
- Great hard to find information
- Engaging, Infuriating, Always Challenging
- Excelent
- A nic book on the philosophy of Bayesian probability theory.
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Probability Theory: The Logic of Science
E. T. Jaynes
Manufacturer: Cambridge University Press
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ASIN: 0521592712 |
Book Description
Going beyond the conventional mathematics of probability theory, this study views the subject in a wider context. It discusses new results, along with applications of probability theory to a variety of problems. The book contains many exercises and is suitable for use as a textbook on graduate-level courses involving data analysis. Aimed at readers already familiar with applied mathematics at an advanced undergraduate level or higher, it is of interest to scientists concerned with inference from incomplete information.
Customer Reviews:
On first reading.......2007-09-09
This is a great book. Getting it all together is well worth the price. Jaynes is always a joy to read, polemical and opinionated as he is. One of the very few writers who can put drama into the dry subject of statistics. This is a book about the subject of statistics, rather than a statistics book, with a lot of critical thought and criticism of other statisticians, and statistical paradoxes. It's not, however, the book to choose if you just want another text to help you pass your stats course as its more about the why rather than the how of statistical thinking and logic.
Great hard to find information .......2007-07-16
Its hard to write a review for this book. There are definitely flaws, but the information in this book, is just not anywhere else. This is the first place I had ever seen a general form of the rule of succession, or a worthwhile logical attack on the Copenhagen interpretation. It is a very interesting and thought provoking book, but is also a good practical reference for advanced probability problems.
Engaging, Infuriating, Always Challenging.......2006-08-15
I've never seen another book like this. Jaynes definitely has an agenda, but he justifies his viewpoint through an amazingly deep tour of probability theory. Not every viewpoint he expresses is convincing (such as his view that quantum theory is inherently probabilistic only because physicists are lazy), but he always raises deep and interesting questions while teaching the ideas. If you can read a book and accept some but not all of its viewpoint, then this is the book on probability for you.
Excelent.......2006-02-28
It is a book between phylosophy and statistic. Clear concepts and easy to understand.
A nic book on the philosophy of Bayesian probability theory........2005-09-13
I read the draft of this book before its publication, which was freely available online at that time. It is worthy, at least, 4 stars.
[1] The author is an important person in the history of Bayesian probability, who firmly believed subjective Bayesian and argued for his belief with those frequentists in his whole life.
[2] It is a philosophy book rather than a textbook of probability. Therein, it is a more valuable work that will surely influence Bayesian theory.
[3] Bayesian inference in theoretical physics may enlighten mathematicians as to a wider and deeper understanding of Bayesianism.
Books:
- The Classical Theory of Fields, Fourth Edition: Volume 2 (Course of Theoretical Physics Series)
- The Craft of Scientific Presentations: Critical Steps to Succeed and Critical Errors to Avoid
- The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling (Second Edition)
- The Geometry of Physics: An Introduction, Second Edition
- The Illustrated on the Shoulders of Giants: The Great Works of Physics and Astronomy
- The Intention Experiment: Using Your Thoughts to Change Your Life and the World
- The Periodic Table: Its Story and Its Significance
- The Periodic Table: Its Story and Its Significance
- The Quantum Dice: An Introduction to Stochastic Electrodynamics (Fundamental Theories of Physics)
- The Roy Adaptation Model
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