was pleased to be able to make Bruce Worton’s appointment permanent;
he was an excellent teacher, particularly on a large service course
to the biologists, and I’d been intrigued by the bootstrap ever
since I’d listened to Bradley Efron talking about it in Monterey in
the early 1980s. I was also delighted to be able to bring in my
erstwhile research assistant Orestis Papasouliotis from Madison as
the research associate in our hastily-devised STATLAB. I became its
first director, and several other statisticians in the department
STATLAB’s immense success was to later on be seriously undervalued
by our Faculty Office. In hindsight, I believe that we achieved
monumental heights. Bruce Worton and I started off by helping Dean
Geoffrey Boulton to analyse his educational foresight data, and he
extrapolated our conclusions all over the place.
Bruce did some useful research on the frequencies of bird species
for RSPB, which also contributed to STATLAB’s success. I got him
started with some theoretical suggestions for a Bayesian modeling
problem, and he later published some business applications, with
George Stretfaris, as well as publishing some applications of other
methodology with BIOSS. He and Orestis later co-published their
analysis of the Scottish glaucoma data. [see item (8) in my CDC
Orestis and I completed a major consultancy for Northumbria police
by analysing aggregated patient records pertaining to a multiple
murder inquiry (a nurse in Newcastle was suspected of routinely
overdosing her patients).
During our complicated analysis we discovered that Bayes factors,
when comparing a one-sided null hypothesis with an oppositely
one-sided alternative hypothesis, can work well in frequency terms
and that Lindley’s paradox can be avoided. While we came up with a
number of significant conclusions, with the help of several jolly
police officers visiting from Newcastle including the superintendent
himself, the nurse was acquitted, possibly as a result of our
STATLAB also made substantial contributions to the template
modelling of the amount of mutton in sheep, and to the Bayesian
statistical analysis in Astronomy of data relating to ionising flux
in high redshifts. See items (2) and (6) of our CDC section.
Orestis proceeded to develop a Bayesian approach to the analysis of
covariance, as his Ph.D. topic in Edinburgh, using judicious
applications of MCMC, and he applied this to a large forensic
science data set contrasting different types of criminals that was
provided to us by Tony Busutil, the University’s Professor of
The conclusions are described in some detail by Orestis in
item (8) of our CDC section. They suggested that paedophiles could
not be detected by neuropsychological tests, given the data then
available. I’d been previously unaware that they were published by
Leonard and Papasouliotis (2002) in the Encyclopaedia of
Environmetrics, and it was a pleasant surprise to discover that
these contributions had been recognized in the literature.
Orestis and I also developed a Bayesian backwards prediction
procedure that drew inferences about the amounts of drugs smuggled
in by criminals during previous trips into the country, given the
amounts that’d recently been discovered in their stomachs. I
co-authored a long paper in JRSSA with Orestis et al,
which described our new methodology.
We later developed a random effects multivariate
generalisation, during a congenial visit by Alan Izenman from Temple
University, which accommodated measurement error when taking
replicated readings of the amounts of drugs.
Unfortunately, we discovered in hindsight that Lothian and
Borders police had provided their contact person Colin Aitken with
extremely precise measurements whose errors were so small that we
were unable to develop a meaningful practical example! But a feather
in the cap for the police. And Alan had a great time with us anyway.
a major component of his duties for STATLAB, Orestis analysed
numerous data sets from around the university, including one that
was left outside his door in a brown bag by a girl with strange
views of Statistics. Highlights included an application of
Laplacian approximations to data in Astrophysics that simulated new
star systems, and an application of Geoff McLachlan’s package for
multivariate mixtures to an analysis of the Lepenski Vir
archaeological data, which reported the nitrogen and carbon content
of Mesolithic and Neolithic skeletons.
The Lepenski Vir results are briefly described on page 3
of my 1999 book with John Hsu, and we came up with a clustering that
wasn’t anticipated by the traditionally-minded archaeologists who
always expected the data to divide neatly into two groups.
later developed a modified Bayesian solution to the Fieller-Creasy
problem that enabled Orestis to show, by analysing an exhaustive
stream of datasets, that pronounced cytoplasmic pH gradients are not
required in plant and fungal cells. We reported these results, with
Parton, Read et al, in the Journal of Cell Science (1997).
Orestis extended his long list of publications when he moved to
Serono International, a pharmaceuticals company in Geneva in 2001.
He now has a senior appointment with them, and hopes to return to
Edinburgh soon with his daughter to visit the zoo.
John Hsu visited us from UCSB on several occasions, and was
appointed to an honorary fellowship in our department. In 1996,
John, Li Sun, Irwin Guttman, John and I published a paper in JASA
that used Laplacian approximations to marginal posterior densities
for parameters of interest in normal random effects models, and also
refuted the 1972 Lindley-Smith estimates (Irwin was fascinated by
this, though I was bored to tears about the damned things by know).
An earlier version had been well-received, with a great deal of
laughter, when I presented it at the founding 1993 San Francisco
meeting of ISBA.
During 1997, two of the co-authors of the 1996 JASA
paper were nevertheless irrationally attacked from a source that
I’ve been asked to say was unknown, but it wasn’t Dennis Lindley and
one of us suffered the consequences. While I was gratuitously
insulted in the most personal terms, which seemed to make an
assumption about my orientation, it’s difficult to prove cause and
effect. I’d hoped that the Leonard-Lindley-Smith saga had been put
to bed, but it would rear its head again briefly in 1998 at Valencia
6 before the alcohol and John Deely took over.
been delighted to be able to appoint Angelika van der Linde from
Bremen to a lectureship in the department on the recommendation of
Bernie Silverman, and this was supposed to be a research
appointment. She’d published some seminal theoretical results on
Gaussian prior processes for regression models, and she proceeded to
develop DIC with David Spiegelhalter, and powerful methods using
reference priors with Jose Bernardo, who visited us later. She was
later of great service to ISBA, and in particular worked as
production editor for the Society’s international journal. She was
also a wonderful lunch-time companion.
Angelika was dumbstruck when the mathematicians asked her
to expend a large amount of her work effort preparing model homework
and exam answers, rather than spending her evenings attending the
film festivals. The undergraduates would typically wait for the
model answers without doing their homework, and then complain if an
exam question didn’t relate to a previously circulated solution.
Many of them didn’t take lecture notes either!
their final year, many of the students’ scores were ratcheted up by
the department’s Scrutiny Committee. This ensured that most
third-class students received upper seconds and their meal tickets.
We were always under strong pressure from the financially-orientated
Faculty Office not to fail any students whatsoever]
1998, Angelika and I travelled to the Sixth Bayesian Valencia
Conference together, a nostalgic return for me after nineteen years,
and it was good to see Peter Freeman again. The conference was held
in the Hotel Las Fuentes on the Spanish Mediterranean coast, just
like the first one, though the seafood wasn’t quite as appetizing,
and I enjoyed the cold water springs on the beach, where I played
football with Richard Smith from North Carolina and chatted about
the fundamentalist Bayesians at Warwick with Peter Lee, the
colourful Provost of Wentworth College from York, while Adrian
Smith, still looking remarkably young, was kissing the ladies.
Dennis Lindley was at his charming best. In 2002, he would
receive the Royal Statistical Society Guy Medal in Gold from Peter
Green for his pioneering developments in Bayesian Statistics, fully
twenty-six years after his retirement.
overriding concern during the poster sessions was that far too many
doctoral students were using MCMC when analysing models that would
have benefited from a more direct prior-to-posterior analysis; they
were therefore wasting their talents.
However, Gelfand and Smith were very much revered in this respect,
and I came across as an old codger with eccentric opinions.
Steve Fienberg teased me with remorseless good humour during his
after-dinner speech, and brought the house down with a series of
jokes regarding my 1975 paper in Technometrics. Jim Zidek
thought that I came out of it well.
Later that evening, I played the Rev. Thomas Bayes
returning from Heaven in a skit prepared by Tony O’Hagan, the Master
of Ceremonies of an otherwise musical cabaret. My adventures with
the wandering microphone brought the house down once again. The
script has been published on the Internet by Brad Carlin, and a
picture of me being interviewed by Tony can be tracked down by
sourcing Pictorial Tribute: Bayesian 6 Cabaret. Life is a cabaret,
my friends, life is a cabaret!
After all that, John Hsu and I finally completed our book
Bayesian Methods: An Analysis for Statisticians for
Interdisciplinary Research Workers (1999), the fourth advanced
graduate text in the Cambridge Series in Statistical and
Probabilistic Mathematics, published by Cambridge University
The book was well received by the critics, with some
quibbling about its level of difficulty and its focus on the whole
gamut of Bayesian marginalization procedures, including MCMC. The
book has sold well (about 5000 copies), with a special edition in
China, and it’s still being used by Kam Tsui at the University of
Wisconsin as part of the course materials for Statistics 775.
Moreover, Professor Charles Franklin used it for several years as
the core text for his UW graduate course for political scientists.
Michael J. Evans of the University of Toronto wrote in
Mathematical Reviews that ‘This book provides excellent
up-to-date coverage of modern Bayesian statistics. The book is
clearly written and at a reasonably high level. Outstanding features
of the text include a number of significant applications of the
methodology, the use and comparison of the AIC and BIC model
selection criteria in several contexts, a somewhat unique treatment
of the axiomatization of Bayesian decision theory through utility,
discussion of the frequency properties of the procedures, and
interesting and relevant self-study exercises that extend and deepen
the content of the book.’