Archive for Péru
women’s circle [women behind the lens]
Posted in pictures, Running, Statistics, Travel with tags Ana Elisa Sotelo, femicides, Gender-Based Violence, Lima, Péru, photography, swimmers, The Guardian, UNiTE to End Violence against Women, Women behind the lens on August 9, 2026 by xi'anParalympiques au Grand Palais
Posted in pictures, Running, Travel with tags air conditioning, bronze, Champs-Elysées, Communist Party of China, gold medal, Grand Palais, gwenn ha du, K44, obscurantism, Paris, Paris 2024 Olympics, Paris 2024 Paralympics, Péru, PRC, refugees, Refugees Team, repêchage, Rio 2016, Seine, taekwondo, Taipei, Taiwan, taliban, theocracy, Tokyo 2021 Olympics, Zakia Khudadadi on September 1, 2024 by xi'an
While we did not try to attend any of the Paris 2024 Olympics events, primarily for being in the Pacific Northwest during the Olympics (in not in Olympic National Park!), being back in Paris and tickets being widely available and looking forward attending an event in the Grand Palais, we found tickets for the taekwondo K44 finals, in the light(er than me) categories. With no prior exposure to taekwondo.
The location was fabulous, indeed, with a full stadium built inside and surprisingly good views from the back seats. (And too much air conditioning!) The rounds were also most interesting, with a wide range of fighting styles, if not always exciting as some ended up with very few points and a lot of referees’ paste&cuts of glued opponents. As in fencing, it was also difficult to understand why some punches scored and others did not. Two games ended up with the judges’ ruling, one rather clearly picking the dominant fighter (but with the looser refusing to shake hands with the winner!), and the other much less so. The only remaining French athlete was badly injured and got eliminated in the first round.
Another great moment was the bronze medal of Zakia Khudadadi, part of the Refugee Paralympics Team, who is an Afghan athlete evacuated to France when the Taliban took power in 2021. She had already competed at the Tokyo 2020 Olympics, a week after her escape from Hell. (Her coach seen carrying her on the picture is Rio 2016 silver medallist Haby Niare from France.) She joins female boxer Cindy Ngamba who won the first-ever medal for the Olympic Refugee Team a few weeks earlier.
The medal ceremony was somewhat low key (esp. from the back seats) but I discovered there taekwondo rules produce two bronze medals, one coming from repêchages. And heard the Peruvian anthem for the first time, thanks to the victory of (Tokyo gold medallist) Leonor Angelica Espinoza Carranza. All in all, this was a great afternoon at the Paralympics, with an unexpectedly efficient organisation, no queues!, cheerful volunteers all over and rather laid-back police officers (in large numbers).
where K. works
Posted in Books, Mountains, pictures, Statistics, Travel, University life with tags back page, canoe, field trip, jaguars, Kerrie Mengersen, Nature, Péru, tribune, virtual reality on December 2, 2019 by xi'an
Savage-Dickey supermodels
Posted in Books, Mountains, pictures, Statistics, Travel, University life with tags astrostatistics, Bayes factor, Biometrika, Brad Carlin, bridge sampling, cosmology, encompassing model, MCMC, mixtures of distributions, nested sampling, Péru, Sid Chib on September 13, 2016 by xi'an
A. Mootoovaloo, B. Bassett, and M. Kunz just arXived a paper on the computation of Bayes factors by the Savage-Dickey representation through a supermodel (or encompassing model). (I wonder why Savage-Dickey is so popular in astronomy and cosmology statistical papers and not so much elsewhere.) Recall that the trick is to write the Bayes factor in favour of the encompasssing model as the ratio of the posterior and of the prior for the tested parameter (thus eliminating nuisance or common parameters) at its null value,
B10=π(φ⁰|x)/π(φ⁰).
Modulo some continuity constraints on the prior density, and the assumption that the conditional prior on nuisance parameter is the same under the null model and the encompassing model [given the null value φ⁰]. If this sounds confusing or even shocking from a mathematical perspective, check the numerous previous entries on this topic on the ‘Og!
The supermodel created by the authors is a mixture of the original models, as in our paper, and… hold the presses!, it is a mixture of the likelihood functions, as in Phil O’Neill’s and Theodore Kypraios’ paper. Which is not mentioned in the current paper and should obviously be. In the current representation, the posterior distribution on the mixture weight α is a linear function of α involving both evidences, α(m¹-m²)+m², times the artificial prior on α. The resulting estimator of the Bayes factor thus shares features with bridge sampling, reversible jump, and the importance sampling version of nested sampling we developed in our Biometrika paper. In addition to O’Neill and Kypraios’s solution.
The following quote is inaccurate since the MCMC algorithm needs simulating the parameters of the compared models in realistic settings, hence representing the multidimensional integrals by Monte Carlo versions.
“Though we have a clever way of avoiding multidimensional integrals to calculate the Bayesian Evidence, this new method requires very efficient sampling and for a small number of dimensions is not faster than individual nested sampling runs.”
I actually wonder at the sheer rationale of running an intensive MCMC sampler in such a setting, when the weight α is completely artificial. It is only used to jump from one model to the next, which sound quite inefficient when compared with simulating from both models separately and independently. This approach can also be seen as a special case of Carlin’s and Chib’s (1995) alternative to reversible jump. Using instead the Savage-Dickey representation is of course infeasible. Which makes the overall reference to this method rather inappropriate in my opinion. Further, the examples processed in the paper all involve (natural) embedded models where the original Savage-Dickey approach applies. Creating an additional model to apply a pseudo-Savage-Dickey representation does not sound very compelling…
Incidentally, the paper also includes a discussion of a weird notion, the likelihood of the Bayes factor, B¹², which is plotted as a distribution in B¹², most strangely. The only other place I met this notion is in Murray Aitkin’s book. Something’s unclear there or in my head!
“One of the fundamental choices when using the supermodel approach is how to deal with common parameters to the two models.”
This is an interesting question, although maybe not so relevant for the Bayes factor issue where it should not matter. However, as in our paper, multiplying the number of parameters in the encompassing model may hinder convergence of the MCMC chain or reduce the precision of the approximation of the Bayes factor. Again, from a Bayes factor perspective, this does not matter [while it does in our perspective].
statistics with jaguars
Posted in pictures, Statistics, Travel, University life with tags field trip, jaguars, Péru, QUT on April 8, 2016 by xi'anFollowing a field trip to Péru of my friend Kerrie Mengersen and other researchers from QUT, to study jaguar population, The Australian ran a story about this most exotic expedition. (Although Kerrie is familiar with exotic topics, since she also worked on cheetahs in Africa and orang-utans in Indonesia.) While the newspaper does not get into the details of the mathematical model used to describe the jaguar population, it turns quite lyrical about the expedition itself and the interactions with the local human populations. In the end it does not sound that terrible that the group could not see any jaguar, despite evidence of a strong presence in the area, as everyone came back unscathed!
