Arnaud Doucet, Sylvain Rubenthaler and I have just put a technical report on arXiv about estimating the first- and second-order derivatives of the log-likelihood (also called the score and the observed information matrix respectively) in general (intractable) statistical models, and in particular in (non-linear non-Gaussian) state-space models. We call them “derivative-free” estimates because they can be computed even if the user cannot compute any kind of derivatives related to the model (as opposed to e.g. this paper and this paper). Actually in some cases of interest we cannot even evaluate the log-likelihood point-wise (we do not have a formula for it), so forget about explicit derivatives. Would you like to know more?
Just a quick note about BayesComp, a new wiki about Bayesian Computational Statistics (see this outdated but well-written introduction if you really don’t know what that is), as Xian pointed out. It is organised by the ISBA Section on Bayesian Computation, notably Peter Green and Nicolas Chopin so far. If the community gets into it, it could become the nerve centre for online resources about Bayesian Computation, which so far are quite scattered and poorly advertised.
Good luck to BayesComp!
New Pathways to Understanding and Managing Marine Ecosystems: Quantifying Uncertainty and Risk Using Biophysical-Statistical Models of the Marine Environment
and of course Happy New Year (2013 is the international year of statistics!).
Last week the ISBA Regional Meeting was held in Banaras / Varanasi, in the North of India. The conference was well attended, with leading figures such as Jayanta K. Ghosh, José Bernardo, James Berger, Peter Green, Christian Robert who blogged about it, and an overall ~350 participants.
With Robin Ryder we wrote a paper titled The Wang-Landau Algorithm Reaches the Flat Histogram in Finite Time and it has been accepted in Annals of Applied Probability (arXiv preprint here). I’m especially happy about it since it was the last remaining unpublished chapter of my PhD thesis. In this post I’ll try to explain what we proved here on a simple example.
What do you do when you see the word “condom” in the title of a new arXiv entry?! You click with wild excitement of course! And you end up reading
At Statisfaction’s headquarters (located inside a volcanic crater on a distant planet), we received an email from Jeffrey Myers from the American Statistical Association to advertise the International Year of Statistics, 2013!
To quote the webpage:
The goals of Statistics2013 include:
- increasing public awareness of the power and impact of Statistics on all aspects of society;
- nurturing Statistics as a profession, especially among young people; and
- promoting creativity and development in the sciences of Probability and Statistics
Those are great goals that we obviously support! Statistics is an important field of applied mathematics and has been for a while now, but public awareness still has to increase. At cocktail parties, it still isn’t super sexy to admit that you’re a statistician. It should be! And it’s good that some people are working on that at Amstat, at Tumblr, at NYTimes, at Rstudio and elsewhere.
We’ll go on blogging here, maybe with new contributors and more technical posts shortly. Stay tuned!
On this useful series of posts from Freakonometrics:
I stumbled upon this 1996 article published in Ecological Applications:
It was a really fun and surprising read to me, so I felt like sharing. Most surprising was the argument that established Frequentism had a better track record than Bayesian stats. What a weird remark from a researcher! Hopefully the atmosphere among ecologists changed since 1996 (and people learned about Bayesian model choice), but I think that such articles explains why experienced Bayesian statisticians spend time writing replies like “Not only defended but also applied”: The perceived absurdity of Bayesian inference and the recently-arXived anti-Bayesian moment and its passing for instance.
After this long and idle summer, here’s a little update of my research life™.
After having completed my PhD (Xi’an and Robin kindly blogged about it there and there) in France, I am now a Research Fellow at the National University of Singapore (NUS), in the Department of Statistics and Applied Probability. I’m going to work mostly with Ajay Jasra on Sequential Monte Carlo theory and methodology. NUS seems like the perfect place to work long hours: there’s space, whiteboards, printers, air conditioning, food courts and even a gym. There’s also a bunch of very prestigious statisticians here but I still don’t know how much interaction I can expect with them. I still plan to blog here about conference, papers, software, etc.
It seems like a good time to give my final impressions about getting a PhD in France, before I forget. All in all, I can’t complain about my personal case: it was a wonderful time for me, mostly thanks to Xi’an.