Archive for John Deely

John J Deely (1933-2025)

Posted in University life with tags , , , , , , , , , , , on February 13, 2025 by xi'an

I just learned that my friend John Deely died last month in West Lafayette. I had come to know him in 1988, when we were both visiting Purdue and sharing an office. He was funny and witty, with a unique and unforgetable laugh, and I remember several evenings with him and Ann. After leaving Purdue, I met John at Bayesian conferences (incl. the Valencia meetings). He had left Christchurch when I visited the University of Canterbury and I never got back to Purdue where he finished his carreer. Our last meeting took place in 2013, for the O’Bayes 2013 conference in Duke. Sadly impacted by the passing away of Dennis Lindley. Farewell, John, for this long trip on the Bayes bus!

[Here the obituary posted on the Purdue Statistics Department Website]

Dr. John Joseph Deely, 92, retired Continuing Lecturer in the Department of Statistics, passed away on January 17, 2025, at his home in West Lafayette, Indiana. Over his decades-long career, John shaped the field of statistics through his teaching, research, and mentoring, leaving an indelible impact on his students, colleagues, and the broader academic community. He will be deeply missed.

John was born in Cleveland, Ohio, on January 13, 1933. He earned an Electrical Engineering degree from Georgia Tech before pursuing his passion for mathematics and statistics at Purdue University, where he completed his M.S. in Mathematics in 1958 and a Ph.D. in Statistics in 1965 under the supervision of Professor Shanti S. Gupta. John’s career took him from Sandia Corporation in Albuquerque, where he worked on cutting-edge research in the space age, to the University of Canterbury in New Zealand, where he spent 28 years growing the statistics program and mentoring generations of students. Under his leadership, the program expanded from a single course with 100 students to a thriving department with eight courses and hundreds of students. In recognition of his contributions, he was promoted rapidly, ultimately serving as Chair of Statistics.

In 1997, John returned to Purdue, where he became a beloved Continuing Lecturer, primarily teaching STAT 113: Statistics and Society. With his sharp wit and engaging teaching style, he introduced thousands of students to the power of statistical reasoning. In his words, “They hate numbers. They hate data. The challenge is can you somehow give such students a little appreciation in statistical reasoning in spite of their hatred of numbers. Can you make it interesting enough? I’m always looking for funny ways, ways in which people abuse statistics.” He was known for his humor, real-world examples, and ability to make statistics both accessible and compelling. Beyond the classroom, he remained an active researcher, collaborating with colleagues worldwide on Bayesian methods, empirical Bayes, and statistical decision theory. His presence at Bayesian meetings often meant that there would be some form of entertainment that involved his ability to make people laugh, as in the presentation of the Delly Awards with Ed George. He retired at the remarkable age of 85.

John is survived by his dear wife of 35 years, Elizabeth “Ann” Young Ann, his six children and their spouses from his first marriage to Anola, eight grandchildren, two great-grandchildren, two stepchildren, and two step-grandchildren. He treasured his family and the life he built with Ann, finding joy in their years together. At this time, no service will be held. May John’s memory bring comfort to all who knew him and inspire future generations in the pursuit of knowledge.

Robert’s paradox [reading in Reading]

Posted in Statistics, Travel, University life with tags , , , , , , , , , , , , on January 28, 2015 by xi'an

paradoxOn Wednesday afternoon, Richard Everitt and Dennis Prangle organised an RSS workshop in Reading on Bayesian Computation. And invited me to give a talk there, along with John Hemmings, Christophe Andrieu, Marcelo Pereyra, and themselves. Given the proximity between Oxford and Reading, this felt like a neighbourly visit, especially when I realised I could take my bike on the train! John Hemmings gave a presentation on synthetic models for climate change and their evaluation, which could have some connection with Tony O’Hagan’s recent talk in Warwick, Dennis told us about “the lazier ABC” version in connection with his “lazy ABC” paper, [from my very personal view] Marcelo expanded on the Moreau-Yoshida expansion he had presented in Bristol about six months ago, with the notion that using a Gaussian tail regularisation of a super-Gaussian target in a Langevin algorithm could produce better convergence guarantees than the competition, including Hamiltonian Monte Carlo, Luke Kelly spoke about an extension of phylogenetic trees using a notion of lateral transfer, and Richard introduced a notion of biased approximation to Metropolis-Hasting acceptance ratios, notion that I found quite attractive if not completely formalised, as there should be a Monte Carlo equivalent to the improvement brought by biased Bayes estimators over unbiased classical counterparts. (Repeating a remark by Persi Diaconis made more than 20 years ago.) Christophe Andrieu also exposed some recent developments of his on exact approximations à la Andrieu and Roberts (2009).

Since those developments are not yet finalised into an archived document, I will not delve into the details, but I found the results quite impressive and worth exploring, so I am looking forward to the incoming publication. One aspect of the talk which I can comment on is related to the exchange algorithm of Murray et al. (2006). Let me recall that this algorithm handles double intractable problems (i.e., likelihoods with intractable normalising constants like the Ising model), by introducing auxiliary variables with the same distribution as the data given the new value of the parameter and computing an augmented acceptance ratio which expectation is the targeted acceptance ratio and which conveniently removes the unknown normalising constants. This auxiliary scheme produces a random acceptance ratio and hence differs from the exact-approximation MCMC approach, which target directly the intractable likelihood. It somewhat replaces the unknown constant with the density taken at a plausible realisation, hence providing a proper scale. At least for the new value. I wonder if a comparison has been conducted between both versions, the naïve intuition being that the ratio of estimates should be more variable than the estimate of the ratio. More generally, it seemed to me [during the introductory part of Christophe’s talk] that those different methods always faced a harmonic mean danger when being phrased as expectations of ratios, since those ratios were not necessarily squared integrable. And not necessarily bounded. Hence my rather gratuitous suggestion of using other tools than the expectation, like maybe a median, thus circling back to the biased estimators of Richard. (And later cycling back, unscathed, to Reading station!)

On top of the six talks in the afternoon, there was a small poster session during the tea break, where I met Garth Holloway, working in agricultural economics, who happened to be a (unsuspected) fan of mine!, to the point of entitling his poster “Robert’s paradox”!!! The problem covered by this undeserved denomination connected to the bias in Chib’s approximation of the evidence in mixture estimation, a phenomenon that I related to the exchangeability of the component parameters in an earlier paper or set of slides. So “my” paradox is essentially label (un)switching and its consequences. For which I cannot claim any fame! Still, I am looking forward the completed version of this poster to discuss Garth’s solution, but we had a beer together after the talks, drinking to the health of our mutual friend John Deely.