Hélène Massam (1949-2020)

I was much saddened to hear yesterday that our friend and fellow Bayesian Hélène Massam passed away on August 22, 2020, following a cerebrovascular accident. She was professor of Statistics at York University, in Toronto, and, as her field of excellence covered [the geometry of] exponential families, Wishart distributions and graphical models, we met many times at both Bayesian and non-Bayesian conferences  (the first time may have been an IMS in Banff, years before BIRS was created). And always had enjoyable conversations on these occasions (in French since she was born in Marseille and only moved to Canada for her graduate studies in optimisation). Beyond her fundamental contributions to exponential families, especially Wishart distributions under different constraints [including the still opened 2007 Letac-Massam conjecture], and graphical models, where she produced conjugate priors for DAGs of all sorts, she served the community in many respects, including in the initial editorial board of Bayesian Analysis. I can also personally testify of her dedication as a referee as she helped with many papers along the years. She was also a wonderful person, with a great sense of humor and a love for hiking and mountains. Her demise is a true loss for the entire community and I can only wish her to keep hiking on new planes and cones in a different dimension. [Last month, Christian Genest (McGill University) and Xin Gao (York University) wrote a moving obituary including a complete biography of Hélène for the Statistical Society of Canada.]

Banff workshop [BIRS 12w5105 meeting [#2]]

Today the program of 12w5105 was more on the theoretical side with adaptive MCMC in the morning and ABC in the afternoon. Éric Moulines and Gersende Fort shared a talk on two papers, one on adaptive tempering and the other one on equi-energy sampling, then Nando de Freitas spoke first about Gaussian process approximation for Bayesian optimisation, then about an adaptive Hamiltonian technique called Sardonics. And Jeff Rosenthal concluded the morning with a review of the results ensuring convergence for adaptive MCMC (with a delightful counter-example called Stairways to Heaven that reminded me of an ice climb in Utah!). After my talk, where Scott Sisson made an interesting comment on the difficulty to extend our framework to a large collection of models (since then the summary statistics have to differ), François Perron discussed in highly interesting details several approximation techniques for the Bayesian estimation of copulas and Scott Sisson presented his recent arXiv paper where a rough estimate of the joint posterior is obtained regression-adjustment ABC, and then estimates of each marginal posterior distribution are separately obtained in a lower-dimensional analysis, all this being connected with Bayes linear analysis. (I do not completely get the way summary statistics are selected for each marginal there, which seems to be done by hand. While I understand why using a lower-dimensional statistic helps in improving the approximation of the marginal posteriors and fights the curse of dimensionality, the fact that the joint posterior sample is based on different summary statistics for the different components makes an interesting statistical puzzle. Maybe the copula approach by François in the previous talk could be used at the final stage.) The final talk by Zhiqiang Tan on comparative performances of resampling and subsampling strategies generated a very animated discussion. (All talks being recorded, mine is available as an mp4 video but watch at your own peril!)