Bayesian Core
A Practical Approach to Computational
Bayesian
Statistics
J.M.
Marin & Ch. P. Robert
SpringerVerlag
[site],
New York, 2007, ISBN
0387389792
Goals  Format
 Schedule  Places  Contact
 Slides, codes & datasets  Typos  Reviews
Goals
The
purpose of this book is to provide a selfcontained entry to practical & computational
Bayesian Statistics using
generic examples from the most common models, for a class duration of
about 7 blocks that
roughly corresponds to 12 to 14 weeks of teaching (with 3 hours of lectures per week), depending on the
intended level & the prerequisites imposed on the students. (That timing does
not includes practice, i.e. programming labs, since those may have a variable
duration also depending on
the students’ involvement & their programming
abilities). The emphasis on
practice is
a strong feature of this book in that its primary audience is made of graduate students that need to use
(Bayesian) statistics as a tool to analyse their experiments and/or datasets. The book
should also appeal to scientists
in all fields, given the versatility of the Bayesian tools. It can also
be used for a more
classical Statistics audience when aiming at teaching a quick entry to Bayesian Statistics at the end of an
undergraduate program for instance. (Obviously, it can supplement another textbook on Data
Analysis at the graduate
level.) The
minimal prerequisites for this course are a mastering of basic
Probability theory for
discrete and continuous variables and of basic Statistics (MLE,
sufficient statistics).
Format
The
format of the book is a somehow
sketchy
coverage of the topics, always backed by a motivated problem &
a
corresponding dataset (available on this website), & a detailed
resolution of the inference procedures pertaining to this problem,
sometimes including commented R
programs. Additional cases are also be proposed as
exercises. The current format is therefore self
contained & can
thus serve as a unique textbook for a service course for scientists
aiming at analyzing data the Bayesian way or as an introductory course
on Bayesian Statistics. Contrary
to usual
practice, the exercises are interseeded within the chapters rather than
postponed till the end of each chapter. There are two reasons for this
stylistic choice: first, the results or developments contained in those
exercises are often relevant for upcoming points in the chapter.
Second, they signal to the student (or to any reader) that some
pondering over the previous pages may be useful before moving to the
following topic & so may act as selfchecking gateways.
Schedule
& expectations
A
course corresponding to the book
has been
taught since 2003 in a second year Master program for students aiming
at a professional degree in Data processing & Statistics (at
University Paris Dauphine). The first half of the book was used in a
seven week (intensive) program & students were tested on both
the
exercises (meaning, all exercises) & their (practical)
mastering of
the datasets, the stated expectation being that they should go further than a
mere reproduction of the R
outputs presented in the book. While the students found that the amount
of work required by this course was rather beyond their usual standards
(!), we observed that their understanding & mastering of
Bayesian
techniques was much deeper & more ingrained than in the more
formal
courses their counterparts had had the years before. In short, they
started to think about the purpose of a Bayesian statistical analysis
rather than on the contents of the final test & they ended
building a
true intuition about what the results should look like, intuition that
for instance helped them to detect modelling & programming
errors! In
most subjects, working on Bayesian Statistics from this perspective
developed a genuine interest for the approach & there were
several
occurrences of students that continued using this approach in later
courses or even better in their job.
In
order to check the
progress of the
students, we collected reports at the end of each chapter that
contained both at the resolution of most exercises & an
original
analysis of the datasets supporting the corresponding chapter,
including commented R programs. A solution manual with solutions
to all exercises is available here as well as on Springer webpage [here] for any reader interested, not only for instructors as was previously the case.
Places
where the book is used
Besides Paris Dauphine, this course has been taught by the authors
abroad, namely at the
University of
Canterbury, Christchurch, New Zealand, in the summer of 2006, &
at the Universidad Central
de Venezuela, Caracas, Venezuela, in the fall of 2006. The book has
already been used as a
textbook at
University of British Columbia, Vancouver, &
at Université de Montréal, both in
Canada, as well as Queensland University of Technology, Brisbane,
Australia. It is now used at the University of Bristol, the University
of Canterbury, the University of Massachusset, and [as a reference] at the University of Maryland and
the University of Wyoming.
Contact
details
The authors can be contacted for general comments or questions
& in
particular
for typos that will be posted as soon as they come to the authors. We
however cannot reply to all questions about exercises &
R programs, especially now that a complete solution manual is
available freely and unrestrictedly).
The same lack of support applies for the LaTeX files provided along
with the PDF files only for instructors' convenience. (
Here is the
complete tared+gzipped file for recompiling the slides, along with
figures and macros!)
Christian
Robert, Université Paris Dauphine & CRESTINSEE
email xian
[at]
ceremade.dauphine.fr
Slides,
codes & datasets
Chapters 
Topics 
Slides
pdf
(latex) 
Datasets 
R functions 
R commands 
2. Normal models 
Conditional
distributions, priors, posteriors, improper priors, conjugate
priors, exponential families, tests, Bayes factors, decision theory,
importance sampling 
#2
(tex) 
normaldata
CMBdata
90cntycr.wk1 

#2.txt 
3. Regression and variable
selection 
Gpriors,
noninformative priors, Gibbs
sampling, variable selection 
#3
(tex) 
caterpillar 
#3.R 
#3.txt 
4. Generalised linear models

Probit,
logit and loglinear models, Metropolis
Hastings algorithms, model choice 
#4
(tex) 
bank
airquality 
#4.R 
#4.txt 
5.
Capture–recapture experiments 
Sampling
models, open populations,
accept reject algorithm, Arnason Schwarz
model 
#5
(tex) 
northernpintail
eurodipabc
EuroDipper 
#5.R 
#5.txt 
6.
Mixture models 
Completion,
variable dimensional models,
label switching, tempering, reversible
jump MCMC 
#6
(tex) 
license


#6.txt 
7.
Dynamic models 
AR,
MA and ARMA models, statespace
representation, hidden Markov models, forwardbackward algorithm 
#7
(tex) 
Eurostoxx50
Dnadataset 

#7.txt

8.
Image analysis 
knearestneighbor,
supervised classification,
segmentation, Markov random fields, Potts model 
#8
(tex) 
vision
bank
Menteith 
#8.R 
#8.txt 
Typos
Despite reading and rereading the manuscript, there unfortunately
remain errors of
different magnitudes, from "invisible" typos to modelling mistakes:
please contact one of us if you think you have detected an error. Typos
that have already been corrected in the second printing are given on
this page
on corrected typos of first printing.
Here is the page for
the typos remaining in the second printing.
Book reviews
Reviews of the book have appeared in
 Statistical Papers, April 2008, by Wolgang Polasek [here]
 Journal of the American Statistical Association, March 2008, by Jarrett Barber [here]
 Biometrics, September 2007, by Lawrence Joseph [here]
 Journal of Statistical Software, July 2007, by Joseph
Hilbe [here]
 Journal of Applied Statistics, January 2008, by Pieter Bastiaan Ober [unavailable online]
 Zentralblatt Math, January 2009, by Mauro Gasparini [here]
and authors of personal or journal reviews are welcome to send us their
review for posting. Here is for instance an alternative proposal from
Xiaohui Chen (UBC) on the projection priors in Chapter 3 (pdf file).
^{
}
_{Last updated, April 8, 2008 © Christian P. Robert}