Archive for something is wrong on the Internet

geometric climbing

Posted in Mountains, pictures with tags , , , , , , , , , , , , on August 5, 2021 by xi'an

On the qualifying round for the Tokyo Olympics, the French climber Mickaël Mawem ended up first, while his brother Bassa was the fastest on the speed climb (as a 2018 and 2019 World Champion) but ruptured a tendon while lead climbing and had to be flown back to Paris for a operation. The New York Times inappropriately and condescendingly qualified this first position as being “unexpected” when Mickaël is the 2019 European Champion in bouldering… The NYT is piling up in its belittling by stating that “Anouck Jaubert of France used a second-place finish in speed to squeak into the final¨… (The other French female climber did not make it, despite being one of the first women to reach the 9b level.)

I remain puzzled by the whole concept of mixing the three sports together. As well as by the scoring system, based on a geometric average of the three rankings, which means in particular that the eight finalists will suffer less than in the qualifying round from a poor performance in one of the three climbs (as Adam Ondra for the speed climb). In addition, there is an obscure advantage coming to Adam Ondra for Bassa Mawem cancelling his participation: according to the NYT, “Ondra will receive a bye and an automatic slot in the speed semifinals” meaning “that a likely eighth-place finish in speed — a ranking number that can be hard to overcome in the multiplication of the combined format — will now be no worse than fourth for Ondra”. (The sentence on the strong impact due to the geometric mean is incorrect in that it has less impact that the arithmetic!)

artificial EM

Posted in Books, Kids, R, Statistics, University life with tags , , , , , , on October 28, 2020 by xi'an

When addressing an X validated question on the use of the EM algorithm when estimating a Normal mean, my first comment was that it was inappropriate since there is no missing data structure to anchor by (right preposition?). However I then reflected upon the infinite number of ways to demarginalise the normal density into a joint density

f(x,z;μ)dz = φ(xμ)

from the (slice sampler) call to an indicator function for f(x,z;μ) to a joint Normal distribution with an arbitrary correlation. While the joint Normal representation produces a sequence converging to the MLE, the slice representation utterly fails as the indicator functions make any starting value of μ a fixed point for EM.

Incidentally, when quoting from Wikipedia on the purpose of the EM algorithm, the following passage

Finding a maximum likelihood solution typically requires taking the derivatives of the likelihood function with respect to all the unknown values, the parameters and the latent variables, and simultaneously solving the resulting equations.

struck me as confusing and possibly wrong since it seems to suggest to seek a maximum in both the parameter and the latent variables. Which does not produce the same value as the observed likelihood maximisation.

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