“The United States [of America] needs Greenland for the purpose of National Security. (…) NATO should be leading the way for us to get it (…) [and] becomes far more formidable and effective with Greenland in the hands of the United States [of America]. Anything less than that is unacceptable” — DT, 14 Jan. 2026
“Yeah, there is one [limit to my ability to use American military might]. My own morality. My own mind. It’s the only thing that can stop me. I don’t need international law.” — DT, 08 Jan. 2026
“We have subsidized Denmark, and all of the Countries of the European Union, and others, for many years by not charging them Tariffs, or any other forms of remuneration. Now, after Centuries [sic], it is time for Denmark to give back — World Peace is at stake!” — DT, 17 Jan. 2026
“Considering your Country decided [sic] not to give me the Nobel Peace Prize for having stopped 8 Wars PLUS [sic bis], I no longer feel an obligation to think purely of Peace, although it will always be predominant, but can now think about what is good and proper for the US. Denmark cannot protect that land from Russia or China, and why do they have a “right of ownership” anyway? There are no written documents, it’s only that a boat landed there hundreds of years ago, but we had boats landing there, also. I have done more for NATO than any other person since its founding, and now, NATO should do something for the United States. The World is not secure unless we have Complete and Total Control of Greenland” — DT, 19 Jan. 2026
“Record Cold Wave expected to hit 40 States. Rarely seen anything like it before. Could the Environmental Insurrectionists please explain – whatever happened to global warming?” – DT, 23 Jan 2026
“I personally asked President Putin not to fire into Kyiv and the various towns for a week, and he agreed to do that. It was very nice. A lot of people almost didn’t believe it, but they were very happy about it because they are struggling badly.” – DT, 29 Jan 2026
Archive for snowstorm
live ball
Posted in Mountains, pictures with tags 2020, animal pictures, Highlands, Scotland, snowstorm, The Guardian, wildlife photography on December 31, 2020 by xi'an
a case for Bayesian deep learnin
Posted in Books, pictures, Statistics, Travel, University life with tags Bayesian foundations, Bayesian model choice, Bayesian neural networks, Bayesian variable selection, Berlin Tegel flughafen, marginalisation, model uncertainty, noninformative priors, normalisation, objective Bayes, snowstorm on September 30, 2020 by xi'an
Andrew Wilson wrote a piece about Bayesian deep learning last winter. Which I just read. It starts with the (posterior) predictive distribution being the core of Bayesian model evaluation or of model (epistemic) uncertainty.
“On the other hand, a flat prior may have a major effect on marginalization.”
Interesting sentence, as, from my viewpoint, using a flat prior is a no-no when running model evaluation since the marginal likelihood (or evidence) is no longer a probability density. (Check Lindley-Jeffreys’ paradox in this tribune.) The author then goes for an argument in favour of a Bayesian approach to deep neural networks for the reason that data cannot be informative on every parameter in the network, which should then be integrated out wrt a prior. He also draws a parallel between deep ensemble learning, where random initialisations produce different fits, with posterior distributions, although the equivalent to the prior distribution in an optimisation exercise is somewhat vague.
“…we do not need samples from a posterior, or even a faithful approximation to the posterior. We need to evaluate the posterior in places that will make the greatest contributions to the [posterior predictive].”
The paper also contains an interesting point distinguishing between priors over parameters and priors over functions, ony the later mattering for prediction. Which must be structured enough to compensate for the lack of data information about most aspects of the functions. The paper further discusses uninformative priors (over the parameters) in the O’Bayes sense as a default way to select priors. It is however unclear to me how this discussion accounts for the problems met in high dimensions by standard uninformative solutions. More aggressively penalising priors may be needed, as those found in high dimension variable selection. As in e.g. the 10⁷ dimensional space mentioned in the paper. Interesting read all in all!
stranded
Posted in pictures, Travel with tags airport, Atlanta, flight mode, Florida, Gainesville, Paris, RER, snowstorm, storm, strike, thunderstorm, tornado on January 12, 2020 by xi'an
Scottish sunbathing
Posted in Statistics with tags Andrew Parkinson, British Wildlife Photography Awards, Cairngorms, hare, Highlands, Scotland, snowstorm, The Guardian on January 5, 2019 by xi'an
