Both registration and call for papers have now been posted on the webpage of the 11th International Workshop on Objective Bayes Methodology, aka O-Bayes 15, that will take place in Valencia next June 1-5. The spectrum of the conference is quite wide, as reflected by the range of speakers. In addition, this conference is dedicated to our friend Susie Bayarri, to celebrate her life and contributions to Bayesian Statistics. And in continuation of the morning jog in the memory of George Casella organised by Laura Ventura in Padova, there will be a morning jog for Susie. So register for the meeting and bring your running shoes!
Archive for Spain
The next O’Bayes meeting (more precisely the International Workshop on Objective Bayes Methodology, O-Bayes15), will take place in València, Spain, on June 1-4, 2015. This is the second time an O’Bayes conference takes place in València, after the one José Miguel Bernardo organised in 1998 there. The principal objectives of O-Bayes15 will be to facilitate the exchange of recent research developments in objective Bayes theory, methodology and applications, and related topics (like limited information Bayesian statistics), to provide opportunities for new researchers, and to establish new collaborations and partnerships. Most importantly, O-Bayes15 will be dedicated to our friend Susie Bayarri, to celebrate her life and contributions to Bayesian Statistics. Check the webpage of O-Bayes15 for the program (under construction) and the practical details. Looking forward to the meeting and hopeful for a broadening of the basis of the O’Bayes community and of its scope!
I just heard that our dear, dear friend Susie Bayarri passed away early this morning, on August 19, in Valencià, Spain… I had known Susie for many, many years, our first meeting being in Purdue in 1987, and we shared many, many great times during simultaneous visits to Purdue University and Cornell University in the 1990’s. During a workshop in Cornell organised by George Casella (to become the unforgettable Camp Casella!), we shared a flat together and our common breakfasts led her to make fun of my abnormal consumption of cereals forever after, a recurrent joke each time we met! Another time, we were coming from the movie theatre in Lafayette in Susie’ s car when we got stopped for going through a red light. Although she tried very hard, her humour and Spanish verve were for once insufficient to convince her interlocutor.
Susie was a great Bayesian, contributing to the foundations of Bayesian testing in her numerous papers and through the direction of deep PhD theses in Valencia. As well as to queuing systems and computer models. She was also incredibly active in ISBA, from the very start of the Bayesian society, and was one of the first ISBA presidents. She also definitely contributed to the Objective Bayes section of ISBA, especially in the construction of the O’Bayes meetings. She gave a great tutorial on Bayes factors at the last O’Bayes conference in Duke last December, full of jokes and passion, despite being already weak from her cancer…
So, hasta luego, Susie!, from all your friends. I know we shared the same attitude about our Catholic education and our first names heavily laden with religious meaning, but I’d still like to believe that your rich and contagious laugh now resonates throughout the cosmos. So, hasta luego, Susie, and un abrazo to all of us missing her.
Diego Salmerón and Juan Antonio Cano from Murcia, Spain (check the movie linked to the above photograph!), kindly included me in their recent integral prior paper, even though I mainly provided (constructive) criticism. The paper has just been arXived.
A few years ago (2008 to be precise), we wrote together an integral prior paper, published in TEST, where we exploited the implicit equation defining those priors (Pérez and Berger, 2002), to construct a Markov chain providing simulations from both integral priors. This time, we consider the case of a binomial regression model and the problem of variable selection. The integral equations are similarly defined and a Markov chain can again be used to simulate from the integral priors. However, the difficulty therein follows from the regression structure, which makes selecting training datasets more elaborate, and whose posterior is not standard. Most fortunately, because the training dataset is exactly the right dimension, a re-parameterisation allows for a simulation of Bernoulli probabilities, provided a Jeffreys prior is used on those. (This obviously makes the “prior” dependent on the selected training dataset, but it should not overly impact the resulting inference.)
Richard Everitt’s twitter account (@bayesian_stats) signaled this PGM 2012 conference in Granada next September. PGM stands for probabilist graphical models and is thus relevant for a lot of us. (Even though I seem to only know Jim Smith in the plethoric program committee.) The website is well-designed with plenty of pictures, but the above logo somehow put me off! (Quite a minor offence, really!!!)Indeed, if the connection with the Arab roots of Granada was to be made, the proper Arabic characters should have been used, at least for the figures, which would give ۲۰۱۲ instead of the above…
There will be a workshop organised by NIPS at the end of the year (there is a flat prior on the date!) in Spain, Sierra Nevada, on Bayesian nonparametrics. Here is the description:
Bayesian nonparametric methods are an expanding part of the machine learning landscape. Proponents of Bayesian nonparametrics claim that these methods enable one to construct models that can scale their complexity with data, while representing uncertainty in both the parameters and the structure. Detractors point out that the characteristics of the models are often not well understood and that inference can be unwieldy. Relative to the statistics community, machine learning practitioners of Bayesian nonparametrics frequently do not leverage the representation of uncertainty that is inherent in the Bayesian framework. Neither do they perform the kind of analysis — both empirical and theoretical — to set skeptics at ease. In this workshop we hope to bring a wide group together to constructively discuss and address these goals and shortcomings.
(I was a bit surprised by the location name, since Sierra Nevada is a mountain range, but Sierra Nevada is also the name of the skiing mountain station next to Granada. I remember driving there to ski with Peter Müller and Judith Rousseau during the objective Bayes meeting of December 2002 organised by Elias Moreno. Depending on the weather it should thus be possible to ski there. Even at night.)