Archive for the Uncategorized Category
Li, Nott, Fan, and Sisson arXived last week a new paper on ABC methodology that I read on my way to Warwick this morning. The central idea in the paper is (i) to estimate marginal posterior densities for the components of the model parameter by non-parametric means; and (ii) to consider all pairs of components to deduce the correlation matrix R of the Gaussian (pdf) transform of the pairwise rank statistic. From those two low-dimensional estimates, the authors derive a joint Gaussian-copula distribution by using inverse pdf transforms and the correlation matrix R, to end up with a meta-Gaussian representation
where the η’s are the Gaussian transforms of the inverse-cdf transforms of the θ’s,that is,
given that the g’s are estimated.
This is obviously an approximation of the joint in that, even in the most favourable case when the g’s are perfectly estimated, and thus the components perfectly Gaussian, the joint is not necessarily Gaussian… But it sounds quite interesting, provided the cost of running all those transforms is not overwhelming. For instance, if the g’s are kernel density estimators, they involve sums of possibly a large number of terms.
One thing that bothers me in the approach, albeit mostly at a conceptual level for I realise the practical appeal is the use of different summary statistics for approximating different uni- and bi-dimensional marginals. This makes for an incoherent joint distribution, again at a conceptual level as I do not see immediate practical consequences… Those local summaries also have to be identified, component by component, which adds another level of computational cost to the approach, even when using a semi-automatic approach as in Fernhead and Prangle (2012). Although the whole algorithm relies on a single reference table.
The examples in the paper are (i) the banana shaped “Gaussian” distribution of Haario et al. (1999) that we used in our PMC papers, with a twist; and (ii) a g-and-k quantile distribution. The twist in the banana (!) is that the banana distribution is the prior associated with the mean of a Gaussian observation. In that case, the meta-Gaussian representation seems to hold almost perfectly, even in p=50 dimensions. (If I remember correctly, the hard part in analysing the banana distribution was reaching the tails, which are extremely elongated in at least one direction.) For the g-and-k quantile distribution, the same holds, even for a regular ABC. What seems to be of further interest would be to exhibit examples where the meta-Gaussian is clearly an approximation. If such cases exist.
কিন্তু আমরা অপরাজিত
After a nice morning run down Leigh Woods and on the muddy banks of the Avon river, I attended a morning session on hyperspectral image non-linear modelling. Topic about which I knew nothing beforehand. Hyperspectral images are 3-D images made of several wavelengths to improve their classification as a mixture of several elements. The non-linearity is due to the multiple reflections from the ground as well as imperfections in the data collection. I found this new setting of clear interest, from using mixtures to exploring Gaussian processes and Hamiltonian Monte Carlo techniques on constrained spaces… Not to mention the “debate” about using Bayesian inference versus optimisation. It was overall a day of discovery as I am unaware of the image processing community (being the outlier in this workshop!) and of their techniques. The problems mostly qualify as partly linear high-dimension inverse problems, with rather standard if sometimes hybrid MCMC solutions. (The day ended even more nicely with another long run in the fields of Ashton Court and a conference diner by the river…)
Even though I flew through Birmingham (and had to endure the fundamental randomness of trains in Britain), I managed to reach the “High-dimensional Stochastic Simulation and Optimisation in Image Processing” conference location (in Goldney Hall Orangery) in due time to attend the (second) talk by Christophe Andrieu. He started with an explanation of the notion of controlled Markov chain, which reminded me of our early and famous-if-unpublished paper on controlled MCMC. (The label “controlled” was inspired by Peter Green who pointed out to us the different meanings of controlled in French [meaning checked or monitored] and in English . We use it here in the English sense, obviously.) The main focus of the talk was on the stability of controlled Markov chains. With of course connections with out controlled MCMC of old, for instance the case of the coerced acceptance probability. Which happened to be not that stable! With the central tool being Lyapounov functions. (Making me wonder whether or not it would make sense to envision the meta-problem of adaptively estimating the adequate Lyapounov function from the MCMC outcome.)
As I had difficulties following the details of the convex optimisation talks in the afternoon, I eloped to work on my own and returned to the posters & wine session, where the small number of posters allowed for the proper amount of interaction with the speakers! Talking about the relevance of variational Bayes approximations and of possible tools to assess it, about the use of new metrics for MALA and of possible extensions to Hamiltonian Monte Carlo, about Bayesian modellings of fMRI and of possible applications of ABC in this framework. (No memorable wine to make the ‘Og!) Then a quick if reasonably hot curry and it was already bed-time after a rather long and well-filled day!z