Tag Archives: Freakonometrics

19Sep/17

Morning talks – COVEA

Covéa

I am going to Paris today to attend a meeting this morning with the people from the Actinfo Chair that I am now part of for the duration of my post-doc with Arthur Charpentier.

I will present the research on genealogy using collaborative data we have been working on this summer. This will be the occation to speak about what we plan to do with Arthur regarding those data in the near future.

Olivier Wintenberger will also share with us his recent research.

06Sep/17

GEOMED 2017 conference in Porto (Portugal)

Porto 2017

This week I will attend the GEOMED2017 conference in Porto, in Portugal. Researchers from different horizons will be gathering here to attend to some talks about spatial statistics, spatial epidemiology and public health. This will be the perfect occasion for me to learn more on those subjects during the three days of the conferences. I will also attend a workshop given by Lee Duncan from Glasgow University on modelling spatial data in R with the package CARBayes).

17Nov/16

Where’s Waldo? Here he is!


Yesterday, I came across a nice article untitled “Here’s Waldo: Computing the optimal search strategy for finding Waldo“, written by Randal S. Olson. I used the data he shared to apply a correction to the kernel density estimation of Waldo’s location.

In this article, Randal explains that he has devoted some time to try to compute the optimal search strategy for finding Waldo. To that end, he has used some machine learning techniques.
From an image provided by Slate (Here’s Waldo, 2013, by Ben Blatt), Randal S. Olson retrieved the coordinates of 68 different locations of Waldo, and kindly shared the data afterwards.

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30Oct/14

Coordinates of “hot-spots”


Last week I uploaded a note about the paper @freakonometrics and I have submitted again after we added quite a few revisions. We have already made available most of the R code to reproduce our applications (on this GitHub repository). However, we did not share our method to obtain the coordinates of the “hot-spots” areas