It has been a long while since I read such a good fantasy trilogy: the series of Wolfblade, Warrior, and Warlord by Jennifer Fallon is altogether a very pleasant and enticing read! It has most of the attributes of standard heroic fantasy, but to a limited extent, in that political maneuvering is generally more important there than magical or military skills. The plot revolves around the Wolfblade family and its fight to keep the crown against external and internal enemies. The most detailed characters in the novel are from this family, or closely associated with it, and the female characters are quite exceptionally well-rendered, in particular the (young) family matriarch. Some turns in the story are very climactic and unexpected, like the sudden death of several important characters. There are times when the plot is slightly thinner and the pace too slow, while some choices made by the author, like the rather easy acceptance of slavery, are disputable, but this is nonetheless a high quality series that I found hard to put down!
Archive for January, 2009
There is an opening for six researchers positions in Statistics at INRA the French national research institute in agronomy, food sciences, and genetics. In particular, the unit located at Jouy-en-Josas near Paris is looking for a Bayesian statistician. The guidelines are provided in English there and the dealine is February 27. This is a call for a permanent position as a civil servant, but there is no condition on age nor nationality. Of course, the salary is comparable with salaries in universities and research centers in France, so is not much, a gross 2000 euros per month, but the working conditions and the environment are quite interesting. The persons to contact for the Bayesian position are Alain Trubuil and Hervé Monod. (Not me!)
Ps—There is also an opening for a lecturer position in Statistics at Paris Dauphine, not yet posted. The salary is just as bad as the one above and there are restrictions on applicants like being qualified by the CNU (Conseil National des Universités), but anyone interested should contact me.
I only recently figured out that cooking tiramisu was much easier than it looked! All you need is mascarpone cheese and ladyfinger biscuits. You briefly beat the mascarpone with heavy cream, quickly dip the biscuits in strong coffee, build up layers of each, and end up by spreading pure unsweeted cocoa on top. Et voilà!
In connection with the discussion about reference priors for logistic regression posted two weeks ago, Aleks Jakulin pointed out the possibility to embed the slides for Bayesian Core that correspond to our approach to the logistic regression model into this blog, using Slideshare. This is quite handy, thanks!
Here are Chapter 3:
and Chapter 4:
Yesterday night, I finished reading La plus belle escalade du monde by Frédéric Flamant, unfortunately not (yet) translated into English. This little book on the most beautiful climbing (hi)stories is very pleasant to read. Once settled the debate on whether or not such a classification of the “most beautiful climbs” is possible, the preliminary overview of “climbing for beginners”, like the types of climbing difficulty, as well as the evolution of the climbing gear and tools, is well run and the selected 15 climbing stories are crisp but intense (I almost missed my metro station yesterday during Cassin’s climb of Eperon Walker). Everyone will most likely find fault with the absence of a missing North face (including myself for the sorely missing Heckmair’s climb of the Eigerwand), but there is a well-deserved entry on Ben Nevis Point Zero Gully and the invention of modern ice climbing by Scots! The book ends up with a good bibliography and several appendices that are also quite nice, including a list of people who should have been quoted. No picture though, apart from the iconic Totem Pole, which unfortunately is no longer because the upper two-third fell into the Tasman sea. In short, both a good intro and a good entry to specific books like my bedside Heckmair’s My Life and Lynn Hill’s Climbing Free: My Life in the Vertical World.
There is a very long and somehow windy—if often funny—introduction to Bayes’ theorem by a researcher in artificial intelligence. In particular, it contains several Java applets that shows how intuition about posterior probabilities can be wrong. The whole text is about constructing Bayes’ theorem for simple binomial outcomes with two possible causes. It is indeed funny and entertaining (at least at the beginning) but, as a mathematician, I do not see how these many pages build more intuition than looking at the mere definition of a conditional probability and at the inversion that is the essence of Bayes’ theorem. The author agrees to some level about this “By this point, Bayes’ Theorem may seem blatantly obvious or even tautological, rather than exciting and new. If so, this introduction has entirely succeeded in its purpose.” Quite right.
When looking further, there is however a whole crowd on the blogs that seems to see more in Bayes’s theorem than a mere probability inversion, see here and there and there again for examples, a focus that actually confuses—to some extent—the theorem [two-line proof, no problem, Bayes' theorem being indeed tautological] with the construction of prior probabilities or densities [a forever-debatable issue]. The theorem per se offers no difficulty, so this may be due to the counter-intuitive inversion of probabilities as the one found in the example of the first blog. But the fact that people often confuse probabilities of causes and probabilities of effects—i.e. the right order of conditioning—does not require a deeper explanation for Bayes’ theorem, rather a pointer at causal reasoning!