notes in a shoebox
Around Christmas time I presented my first impressions of Kruschke’s Doing Bayesian Data Analysis. This is a very nice book but one of its drawbacks was that part of the code used BUGS, which left mac users like me stuck.
Kruschke has now made JAGS code available so I am happy clappy and looking forward to test this New Year present. In addition, there are other updates available for the programs included in the book.
It must be remembered that there is nothing more difficult to plan, more doubtful of success, nor more dangerous to manage, than the creation of a new system. For the initiator has the enmity of all who would profit by the preservation of the old institutions and merely lukewarm defenders in those who would gain by the new ones.
—Niccolò Machiavelli, The Prince, Chapter 6.
E debbasi considerare, come non è cosa più difficile a trattare, né più dubia a riuscire, né più pericolosa a maneggiare, che farsi capo ad introdurre nuovi ordini; perché lo introduttore ha per nimici tutti quelli che degli ordini vecchi fanno bene, e ha tepidi defensori tutti quelli che delli ordini nuovi farebbono bene.
—Niccolò Machiavelli, Il Principe, Capitolo VI.
I’m a big fan of this quote, because it applies to so many new endeavors including the introduction of new topics in a university curriculum.
About a month ago I was discussing the approach that I would like to see in introductory Bayesian statistics books. In that post I mentioned a PDF copy of Doing Bayesian Data Analysis by John K. Kruschke and that I have ordered the book. Well, recently a parcel was waiting in my office with a spanking new, real paper copy of the book. A few days are not enough to provide a ‘proper’ review of the book but I would like to discuss my first impressions about the book, as they could be helpful for someone out there.
If I were looking for a single word to define the word it would be meaty, not on the “having the flavor or smell of meat” sense of the word as pointed out by Newton, but on the conceptual side. Kruschke has clearly put a lot of thought on how to draw a generic student with little background on the topic to start thinking of statistical concepts. In addition Kruschke clearly loves language and has an interesting, sometimes odd, sense of humor; anyway, Who am I to comment on someone else’s strange sense of humor?
One difference between the dodgy PDF copy and the actual book is the use of color, three shades of blue, to highlight section headers and graphical content. In general I am not a big fun of lots of colors and contentless pictures as used in modern calculus and physics undergraduate books. In this case, the effect is pleasant and makes browsing and reading the book more accessible. Most graphics really drive a point and support the written material, although there are exceptions in my opinion like some faux 3D graphs (Figure 17.2 and 17.3 under multiple linear regression) that I find somewhat confusing.
The book’s website contains PDF versions of the table of contents and chapter 1, which is a good way to whet your appetite. The book covers enough material as to be the sole text for an introductory Bayesian statistics course, either starting from scratch or as a transition from a previous course with a frequentist approach. There are plenty of exercises, a solutions manual and plenty of R code available.
The mere existence of this book prompts the question: Can we afford not to introduce students to a Bayesian approach to statistics? In turn this sparks the question How do we convince departments to de-emphasize the old way? (this quote is extremely relevant)
Verdict: if you are looking for a really introductory text, this is hands down the best choice. The material goes from the ‘OMG do I need to learn stats?’ level to multiple linear regression, ANOVA, hierarchical models and GLMs.
P.S. I’m still using a combination of books, including Krushke’s, and Marin and Robert’s for my own learning process.
P.S.2 There is a lot to be said about a book that includes puppies on its cover and references to A Prairie Home Companion on its first page (the show is sometimes re-broadcasted down under by Radio New Zealand).
Solomon saith, There is no new thing upon the earth. So that as Plato had an imagination, that all knowledge was but remembrance; so Solomon giveth his sentence, That all novelty is but oblivion.
—Francis Bacon: Essays, LVIII quoted by Jorge Luis Borges in The Immortal (1949).
This post is somewhat marginal to R in that there are several statistical systems that could be used to tackle the problem. Bayesian statistics is one of those topics that I would like to understand better, much better, in fact. Unfortunately, I struggle to get the time to attend courses on the topic between running my own lectures, research and travel; there are always books, of course.
After we had some strong earthquakes in Christchurch we have had limited access to most part of our physical library (still had full access to all our electronic collection†). Last week I had a quick visit to the library and picked up three introductory books: Albert’s Bayesian computation with R, Marin and Robert’s Bayesian core: a practical approach to computational Bayesian statistics and Bolstad’s Understanding computational Bayesian statistics (all links to Amazon). My intention was to see if I could use one (or several of them) to start on the topic. What follows are my (probably unfair) comments after reading the first couple of chapters of each book.
In my (highly individual and dubious) opinion Albert’s book is the easiest to read. I was waiting to see the doctor while reading—and actually understanding—some of the concepts. The book is certainly geared towards R users and gradually develops the code necessary to run simple analyses from estimating a proportion to fitting (simple) hierarchical linear models. I’m still reading, which is a compliment.
Marin and Robert’s book is quite different in that uses R as a vehicle (like this blog) but the focus is more on the conceptual side and covers more types of models than Albert’s book. I do not have the probability background for this course (or maybe I did, but it was ages ago); however, the book makes me want to learn/refresh that background. An annoying comment on the book is that it is “self-contained”; well, anything is self-contained if one asks for enough prerequisites! I’m still reading (jumping between Albert’s and this book), and the book has managed to capture my interest.
Finally, Bolstad’s book. How to put this? “It is not you, it is me”. It is much more technical and I do not have the time, nor the patience, to wait until chapter 8 to do something useful (logistic regression). This is going back to the library until an indeterminate future.
If you are now writing a book on the topic I would like to think of the following user case:
It is hard for me to be more selfish in this description; you are potentially writing a book for me.
† After the first quake our main library looked like this. Now it is mostly normal.
P.S. After publishing this post I remembered that I came across a PDF copy of Doing Bayesian Data Analysis: A Tutorial with R and BUGS by Kruschke. Setting aside the dodginess of the copy, the book looked well-written, started from first principles and had puppies on the cover (!), so I ordered it from Amazon.
P.D. 2011-12-03 23:45 AEST Christian Robert sent me a nice email and wrote a few words on my post. Yes, I’m still plodding along with the book although I’m taking a ten day break while traveling in Australia.
P.D. 2011-11-25 12:25 NZST Here is a list of links to Amazon for the books suggested in the comments:
I believe that no one who is familiar, either with mathematical advances in other fields, or with the range of special biological conditions to be considered, would ever conceive that everything could be summed up in a single mathematical formula, however complex.
— R.A. Fisher (1932) quoted in the preface to Foundations of Mathematical Genetics by A.W.F. Edwards (1976).
Before starting the description of the probability distributions, we want to impose on the reader the essential feature that a model is an interpretation of a real phenomenon that fits its characteristics to some degree of approximation rather than an explanation that would require the model to be “true”. In short, there is no such thing as a “true model”, even though some models are more appropriate than others!
— Jean-Michel Marin and Christian P. Robert in Bayesian Core: a practical approach to computational Bayesian statistics.