Archive for the Uncategorized Category

your body, our choice

Posted in Uncategorized with tags , , , on April 2, 2016 by xi'an

After watching Donald Trump apparently shooting himself in the foot with his statement about prosecuting women seeking illegal abortions, it is even more ghastly to hear Ted Cruz’s subsequent declaration:

“On the important issue of the sanctity of life, what’s far too often neglected is that being pro-life is not simply about the unborn child; it’s also about the mother — and creating a culture that respects her and embraces life.”

“Of course we shouldn’t be talking about punishing women; we should affirm their dignity and the incredible gift they have to bring life into the world.”

Hence, from his point of view, it sounds like women are to be respected as wombs, but not much farther than that. Especially not when exercising their free will and (so far) legal rights in seeking an abortion. This reminded me of this older story of a US sheriff depriving a pregnant prisoner of her parental authority to bar her from having an abortion…

[down]value my CV?!

Posted in Uncategorized with tags , , , , , on February 21, 2016 by xi'an

skillsI came by chance to this web service Adzune, which takes CV’s through text mining and returns an estimate of which salary this experience is worth. Here is the summary produced, along with an automated word cloud (food safety?! millennium?! How comes this appears in my skills?).

Christian Robert’s experience appears to be concentrated in Information Technology / Big Data, with exposure to Business Operations and General Business / General Skills and Activities. Christian Robert has 29 years of work experience, with 22 years of management experience, including a high-level position.

The most positive thing one can state about this summary is that the algorithm does not seem very adequate for an academic. Exposure to Business Operations? Me?! Statistics does not seem to be a catchy enough skill for those analysts. Nttt….

Orange the World

Posted in Kids, Uncategorized with tags , , on November 25, 2015 by xi'an

extending ABC to high dimensions via Gaussian copula

Posted in Books, pictures, Statistics, Travel, Uncategorized, University life with tags , , , on April 28, 2015 by xi'an

plane2Li, 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

f(\theta)=\dfrac{1}{|R|^{1/2}}\exp\{\eta^\prime(I-R^{-1})\eta/2\}\prod_{i=1}^p g_i(\theta_i)

where the η’s are the Gaussian transforms of the inverse-cdf transforms of the θ’s,that is,

\eta_i=\Phi^{-1}(G_i(\theta_i))

Or rather

\eta_i=\Phi^{-1}(\hat{G}_i(\theta_i))

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.

je suis Avijit Roy

Posted in Uncategorized with tags , , , , , on February 27, 2015 by xi'an

আমরা শোকাহত
কিন্তু আমরা অপরাজিত

[“We mourn but we are not defeated”]

je suis Raif

Posted in Uncategorized with tags , , , , , , on January 19, 2015 by xi'an

high-dimensional stochastic simulation and optimisation in image processing [day #2]

Posted in pictures, Statistics, Travel, Uncategorized, University life, Wines with tags , , , , , , on August 30, 2014 by xi'an

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…)

 

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