Archive for south Indian cuisine

a journal of the chaos (and rhubarb) year

Posted in Books, Kids, Mountains, pictures, Travel, Wines with tags , , , , , , , , , , , , , , , , , , , , , , , , , , , , on July 30, 2025 by xi'an

 Read Fjellet, geværet, vannet (A mountain, a gun, a lake) by Lars Ramslie (in French, published by the Chamonix based Paulsen editions). A perturbing recollection of a broken childhood between the Norwegian outdoors and violent parenting, in the shadow of an out-of-control father. To the point I found it rather hard to finish the book, the author (and son) being often borderline apologetic for his father’s violent outbursts. That he survived such a trauma is astounding. That local social services could not keep his father away is a shame… I also read E.M. Forster’s A Passage to India classic, while travelling to Chennai, which already reviewed in a separate post.

Beyond indulging in South Indian dishes and a never ending range of appams and local fruits, I made rhubarb preserve from a home-grown rhubarb planting, sometime supplemented with the first figs from our fig-tree and sometime augmented by farmer market sticks. Picked too hot a day to make a ceviche, despite a quick dip in lemon as I returned from the market. (No aftereffect, it just did not taste nice.) And panned an experimental monkfish liver that proved nice and ejnoyable (and with a very mild taste compared with the veal liver I had to eat once a week as a child).

Watched nothing on the flights to and from India. Not even my neighbours’ screens! But watched the Japanese TV series The Hot Spot that is rather funny if not hilarious, and on the lengthy side (esp. all the lunch meetings in Mont Blanc (!) restaurant). Still, the mundane lives of all characters, including the alien—who suffers from all too human shortcomings!—, and  the ever-present backdrop of Mount Fuji make the show worth watching for a while. (A second season feels superfluous, mind.)

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e-values in Chennai

Posted in Books, pictures, Running, Statistics, Travel, University life with tags , , , , , , , , , , , , , , , , , , , on July 23, 2025 by xi'an

To recap, I thus attended the BIRS-CMI workshop 25w5482 at the Chennai Mathematical Institute, Navalur, Tamil Nadu, in early July, for being intrigued by the developments around the concept. And enjoyed the week, from partaking in the company of friendly and enthusiastic academics to the exposure of new views and concepts, mostly remote from mine’s. Recall that an e-value attached to an hypothesis H described as a collection of distributions is a non-negative random variable E with expectation less than 1 for E~Q and all Q ∈ H. When a stopping rule is involved, the e-value is extended into an e-process. (Beyond Aaditya Ramdas’ E-book, Ruodu Wang also wrote a “tiny” review.) Aaditya Ramdas recalled in his introduction of the workshop that e-values are fundamentally equivalent to p-values and confidence intervals. And that a confidence sequence is a sequence of confidence intervals that contains the true value for all time steps t’s with a probability of at least 1-α.

The talks reflected a general belief in α levels and in Neyman-Pearsonian likelihood ratio optimality in simple vs simple settings, considering extension for sequential analysis settings, anytime inference, universality under general alternatives, and connections with FDRs, incl. Benjamini & Hochberg solution, but pointed out a lack of middle ground between frequentists and Bayesians.

“e-values have a clear interpretation in terms of betting and are closely related to likelihood ratios and other Bayes factor. At the same time, e–values do not require prior distributions conditional on the null and alternative hypotheses”

Although David R. Bickel attempted a Bayesian version, using a marginal likelihood ratio within betting settings, that is an incoming American Statistician paper. I may have being missing some aspects due to a lack of sleep the night before (!), but I find the attempt resulting in a fairly unusual vision of Bayesian testing as either not depending on any parameter or on the opposite using a family of priors. I did not understand either the “criticism” that the predictive depends on the prior and felt that this representation was bending in a rather onsiderable way the Bayesian perspective towards achieving a certain degree of agreement with p– and e-value notions, to conclude that the Bayes factor is an e-value. (As an aside, this may be the first paper that cited our critical review of Aitkin! Similarly, Shubhada Agrawal mentioned Roger Farrell in his talk, with whom we wrote a complete class Annals paper in the late 1980’s.) Nikos Ignatiadis also explored Empirical Bayes e-values, while Ben Chugg gave a presentation (constrained) admissibility, albeit under type-I error constraints that makes Bayes infeasible and using Neyman-Pearsonian loss functions. On the last day, Peter Grünwald tried for some BFF cohesion with openings on e-posteriors, treating hypothesis testing losses symmetrically, defining it as an inverse of e-values but incorporating pseudo-posteriors of many flavours like confidence, inferential, and fiducial distributions. He also mentioned a Savage-Dickey version while using an arbitrary prior, which is also an e-value, but with upper & lower meanings, again with measure issues

Given the hosting of the workshop in the Chennai Mathematical Institute, which is quite far from the centre of town (much closer to Mahabalipuram!), I did not visit Chennai but enjoyed the South Indian cuisine (albeit missing some fierceness in the spices!) and local fruits from street stands, if being sorry I could not find cocoa pods from nearby Kerala.

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off to Bengaluru!

Posted in Books, Statistics, Travel, University life with tags , , , , , , , , on December 25, 2022 by xi'an

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high-dimensional stochastic simulation and optimisation in image processing [day #3]

Posted in pictures, Statistics, Travel, University life with tags , , , , , , , , , , , , on August 31, 2014 by xi'an

Last and maybe most exciting day of the “High-dimensional Stochastic Simulation and Optimisation in Image Processing” in Bristol as it was exclusively about simulation (MCMC) methods. Except my own talk on ABC. And Peter Green’s on consistency of Bayesian inference in non-regular models. The talks today were indeed about using convex optimisation devices to speed up MCMC algorithms with tools that were entirely new to me, like the Moreau transform discussed by Marcelo Pereyra. Or using auxiliary variables à la RJMCMC to bypass expensive Choleski decompositions. Or optimisation steps from one dual space to the original space for the same reason. Or using pseudo-gradients on partly differentiable functions in the talk by Sylvain Lecorff on a paper commented earlier in the ‘Og. I particularly liked the notion of Moreau regularisation that leads to more efficient Langevin algorithms when the target is not regular enough. Actually, the discretised diffusion itself may be geometrically ergodic without the corrective step of the Metropolis-Hastings acceptance. This obviously begs the question of an extension to Hamiltonian Monte Carlo. And to multimodal targets, possibly requiring as many normalisation factors as there are modes. So, in fine, a highly informative workshop, with the perfect size and the perfect crowd (which happened to be predominantly French, albeit from a community I did not have the opportunity to practice previously). Massive kudos to Marcello for putting this workshop together, esp. on a week where family major happy events should have kept him at home!

As the workshop ended up in mid-afternoon, I had plenty of time for a long run with Florence Forbes down to the Avon river and back up among the deers of Ashton Court, avoiding most of the rain, all of the mountain bikes on a bike trail that sounded like trail running practice, and building enough of an appetite for the South Indian cooking of the nearby Thali Café. Brilliant!

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