Just a quick note about BayesComp, a new wiki about Bayesian Computational Statistics (see this outdated but well-written introduction if you really don’t know what that is), as Xian pointed out.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
additionally to the referral program (you refer a new user, you win an extra .5 Go), the Dropbox Space Race will give you 3 Go extra space (for 2 years) if you register with your email from a competing university. The best schools will get more space. Here are the 100 top schools. Com’ on, there is no french school in the 100 top !
Thanks Nicolas for the info.
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!
Hi folks !
Last Tuesday a seminar on Bayesian procedure for inverse problems took place at CREST. We had time for two presentations of young researchers Bartek Knapik and Kolyan Ray. Both presentations deal with the problem of observing a noisy version of a linear transform of the parameter of interest
where is a linear operator and a Gaussian white noise. Both presentations considered asymptotic properties of the posterior distribution (Their papers can be found on arxiv, here for Bartek’s, and here for Kolyan’s). There is a wide literature on asymptotic properties of the posterior distribution in direc models. When looking at the concentration of toward a true distribution given the data, with respect to some distance , well known problem is to derive concentration rates, that is the rate such that
For inverse problems, the usual methods as introduced by Ghosal, Ghosh and van der Vaart (2000) usually fails, and thus results in this settings are in general difficult to obtain.
Bartek presented some very refined results in the conjugate case. He manages to get some results on the concentration rates of the posterior distribution, on Bayesian Credible Sets and Bernstein – Von Mises theorems – that states that the posterior is asymptotically Gaussian – when estimating a linear functional of the parameter of interest. Kolyan got some general conditions on the prior to achieve concentration rate, and prove that these techniques leads to optimal concentration rates for classical models.
I only knew little about inverse problems but both talks were very accessible and I will surely get more involved in this field !
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.
This blog is not dead! And it’s gonna get more active soon.
These last few days, a workshop on Sequential Monte Carlo methods was held in the University of Warwick (link to the webpage). It was a very exciting meeting, efficiently organised by Arnaud Doucet, Adam Johansen, Anthony Lee and Murray Pollock and hosted by CRiSM. For those who couldn’t attend, here’s a little summary of my experience (or more exactly, just a bunch of links). Since SMC methods are at the core of my research, I was logically interested by all the talks (which is exceptional for 3 days of workshop, filled with 30 talks!). It was probably a good time for a workshop on SMC, since there’s a lot of recent activity in the field. My impression is that this renewed interest is mainly due to:
- the Particle MCMC framework, which inspired other algorithms (like our SMC^2 that Nicolas Chopin presented, or the Particle Gibbs with ancestral sampling), renewed interest in the normalising constant estimate in particle filters, and more generally made it possible to estimate parameters in a broad class of time series models with plenty of diverse applications (probably more than 200 citations already);
- theoretical advances, with recent papers from Pierre Del Moral, Nick Whiteley, Ajay Jasra, and many others;
- a new class of exact algorithms for simulating continuous time processes without any discretisation error, based on sequential importance sampling, in a fascinating work by Paul Fearnhead, Krzysztof Łatuszyński and colleagues.
- parallel computing, including the recent GPU trend, which makes SMC all the more attractive compared to purely iterative algorithms like MCMC.
The last point was illustrated at this workshop by a recent work from Alexandre Bouchard-Côté and colleagues called “Entangled Monte Carlo”, as well as by my own presentation: I talked about a new resampling scheme that avoids global interactions between all the particles, and resorts only to multiple pair-wise interactions. This is an on-going work with Pierre Del Moral, Anthony Lee, Lawrence Murray and Gareth Peters, that I might talk about again with more details in the future!