# Advances in scalable Bayesian computation [day #2]

**Xi'an's Og » R**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)

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**A**nd here is the second day of our workshop *Advances in Scalable Bayesian Computation* gone! This time, it sounded like the “main” theme was about brains… In fact, Simon Barthelmé‘s research originated from neurosciences, while Dawn Woodard dissected a brain (via MRI) during her talk! (Note that the BIRS website currently posts Simon’s video as being *Dan Simpson’s talk*, the late change in schedule being due to Dan most unfortunately losing his passport during a plane transfer and most unfortunately being prevented from attending…) I found Simon’s talk quite inspiring, with this Tibshirani et al.’s trick of using logistic regression to estimate densities as a classification problem central to the method and suggesting a completely different vista for handling normalising constants… Then Raazesh Sainudiin gave a detailed explanation and validation of his approach to density estimation by multidimensional pavings/histograms, with a tree representation allowing for fast merging of different estimators. Raaz had given a preliminary version of the talk at CREST last Fall, which helped with focussing on the statistical aspects of the method. Chris Strickland then exposed an image analysis of flooded Northern Queensland landscapes, using a spatio-temporal model with changepoints and about 18,000 parameters. still managing to get an efficiency of *O(np)* thanks to two tricks. Then it was time for the group photograph outside in a balmy -18⁰ and an open research time that was quite profitable.

**I**n the afternoon sessions, Paul Fearnhead presented an auxiliary variable approach to particle Gibbs, which again opened new possibilities for handling state-space models, but also reminding me of Xiao-Li Meng’s reparameterisation devices. And making me wonder (out loud) whether or not the SMC algorithm was that essential in a static setting, since the sequence could be explored in any possible order for a fixed time horizon. Then Emily Fox gave a 2-for-1 talk, mostly focussing on the first talk, where she introduced a new technique for approximating the gradient in Hamiltonian (or Hockey!) Monte Carlo, using second order Langevin. She did not have much time for the second talk, which intersected with the one she gave at BNP’ski in Chamonix, but focussed on a notion of sandwiched slice sampling where the target density only needs bounds that can get improved if needed. A cool trick! And the talks ended with Dawn Woodard‘s analysis of time varying 3-D brain images towards lesion detection, through an efficient estimation of a spatial mixture of normals.

Filed under: Books, Mountains, pictures, R, Statistics, University life Tagged: Banff International Research Station for Mathematical Innovation, Bayesian nonparametrics, Canada, Canadian Rockies, cherry tree, Elements of Statistical Learning, finite mixtures, fMRI, Hamiltonian Monte Carlo, histogram, image analysis, particle Gibbs sampler

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