# Statisfaction

## Bayesian workshop in Grenoble, September 6-7

Posted in General, Seminar/Conference by Julyan Arbel on 23 May 2018

We are organising a two-day Bayesian workshop in Grenoble in September 6-7, 2018. It will be the second edition of the Italian-French statistics seminar (link to first edition), titled this year: Bayesian learning theory for complex data modeling. The workshop will give to young statisticians the opportunity to learn from and interact with highly qualified senior researchers in probability, theoretical and applied statistics, with a particular focus on Bayesian methods.

Anyone interested in this field is welcome. There will be two junior sessions and a poster session with a call for abstract open until June 30. A particular focus will be given to researchers in the early stage of their career, or currently studying for a PhD, MSc or BSc. The junior session is supported by ISBA through travel awards.

There will be a social dinner on September 6, and a hike organised in the mountains on September 8.

Confirmed invited speakers

• Simon Barthelmé, Gipsa-lab, Grenoble, France
• Arnoldo Frigessi, University of Oslo, Norway
• Benjamin Guedj, Inria Lille – Nord Europe, France
• Alessandra Guglielmi, Politecnico di Milano, Italy
• Antonio Lijoi, University Bocconi, Milan, Italy
• Bernardo Nipoti, Trinity College Dublin, Ireland
• Sonia Petrone, University Bocconi, Milan, Italy

Important Dates:

• June 30, 2018: Abstract submission closes
• July 20, 2018: Notification on abstract acceptance
• August 25, 2018: Registration closes

More details and how to register: https://sites.google.com/view/bigworkshop

We look forward to seeing you in Grenoble.

Best,

Julyan

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## AI in Grenoble, 2nd to 6th July 2018

Posted in General, Seminar/Conference by Julyan Arbel on 22 March 2018

This is an advertisement for on conference on AI organised at Inria Grenoble by Thoth team and Naver labs : https://project.inria.fr/paiss/. This AI summer school comprises lectures and practical sessions conducted by renowned experts in different areas of artificial intelligence.

This event is the revival of a past series of very successful summer schools which took place in Grenoble and Paris. The latest edition of this series was held in 2013. While originally focusing on computer vision, the summer school now targets a broader AI audience, and will also include presentations about machine learning, natural language processing, robotics, and cognitive science.

Note that NAVER LABS is funding a number of students to attend PAISS. Apply before 4th April. (more…)

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## A big problem in our community

Posted in General, Seminar/Conference, Statistics by Pierre Jacob on 14 December 2017

“Tout va très bien”, meaning “all is well”, by Franquin.

Hi all,

Kristian Lum, who was already one of my Statistics superheroes for her many interesting papers and great talks, bravely wrote the following text about her experience as a young statistician going to conferences:

https://medium.com/@kristianlum/statistics-we-have-a-problem-304638dc5de5

I can’t thank Kristian enough for speaking out. Her experience is both shocking and hardly surprising. Many, many academics report similar stories. This simply can’t go on like that.

I happen to have gone to the conferences mentioned by Kristian, and my experience as a young man was completely different. It was all about meeting interesting people, discussing ideas, being challenged, and having good times. Nobody harassed, touched or assaulted me. There was some flirting, as I guess is natural when hundreds of people are put in sunny places far away from home, but I was never the victim of any misconduct or abuse of power. So instead of driving me out of the field, conferences became important, enriching and rewarding moments of my professional life.

Looking back at those conferences I feel sick, and heartbroken, at the thought that some of my peers were having such a difficult time, because of predators who don’t ever  face the consequences of their actions. Meanwhile I was part of the silent majority.

The recent series of revelations about sexual harassment and assaults in other professional environments indicate that this is not specific to our field, nor to academia. But this does not make it any more acceptable. I know for a fact that many leaders of our field take this issue extremely seriously (as Kristian mentions too),  but clearly much much more needs to be done. The current situation is just shameful; strong and  coordinated actions will be needed to fix it. Thanks again to Kristian for the wake-up call.

## New R user community in Grenoble, France

Posted in R, Seminar/Conference by Julyan Arbel on 13 September 2017

Nine R user communities already exist in France and there is a much large number of R communities around the world. It was time for Grenoble to start its own!

The goal of the R user group is to facilitate the identification of local useRs, to initiate contacts, and to organise experience and knowledge sharing sessions. The group is open to any local useR interested in learning and sharing knowledge about R.

The group’s website features a map and table with members of the R group. Members with specific skills related to the use of R are referenced in a table and can be contacted by other members.  A gitter allows members to discuss R issues and a calendar presents the upcoming events.  (more…)

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## ABC in Banff

Posted in General, Seminar/Conference, Statistics by Pierre Jacob on 6 March 2017

Banff, also known as not the worst location for a scientific meeting.

Hi all,

Last week I attended a wonderful meeting on Approximate Bayesian Computation in Banff, which gathered a nice crowd of ABC users and enthusiasts, including lots of people outside of computational stats, whom I wouldn’t have met otherwise. Christian blogged about it there. My talk on Inference with Wasserstein distances is available as a video here (joint work with Espen Bernton, Mathieu Gerber and Christian Robert, the paper is here). In this post, I’ll summarize a few (personal) points and questions on ABC methods, after recalling the basics of ABC (ahem).

## momentify R package at BAYSM14

Posted in General, R, Seminar/Conference, Statistics by Julyan Arbel on 20 September 2014

I presented an arxived paper of my postdoc at the big success Young Bayesian Conference in Vienna. The big picture of the talk is simple: there are situations in Bayesian nonparametrics where you don’t know how to sample from the posterior distribution, but you can only compute posterior expectations (so-called marginal methods). So e.g. you cannot provide credible intervals. But sometimes all the moments of the posterior distribution are available as posterior expectations. So morally, you should be able to say more about the posterior distribution than just reporting the posterior mean. To be more specific, we consider a hazard (h) mixture model

$\displaystyle h(t)=\int k(t;y)\mu(dy)$

where $k$ is a kernel, and the mixing distribution $\mu$ is random and discrete (Bayesian nonparametric approach).

We consider the survival function $S$ which is recovered from the hazard rate $h$ by the transform

$\displaystyle S(t)=\exp\Big(-\int_0^t h(s)ds\Big)$

and some possibly censored survival data having survival $S$. Then it turns out that all the posterior moments of the survival curve $S(t)$ evaluated at any time $t$ can be computed.

The nice trick of the paper is to use the representation of a distribution in a [Jacobi polynomial] basis where the coefficients are linear combinations of the moments. So one can sample from [an approximation of] the posterior, and with a posterior sample we can do everything! Including credible intervals.

I’ve wrapped up the few lines of code in an R package called momentify (not on CRAN). With a sequence of moments of a random variable supported on [0,1] as an input, the package does two things:

• evaluates the approximate density
• samples from it

A package example for a mixture of beta and 2 to 7 moments gives that result:

## MCM’Ski lessons

Posted in Seminar/Conference by Pierre Jacob on 16 January 2014

A few days after the MCMSki conference, I start to see the main lessons gathered there.

1. I should really read the full program before attending the next MCMSki. The three parallel sessions looked consistently interesting, and I really regret having missed some talks (in particular Dawn Woodard‘s and Natesh Pillai‘s) and some posters as well (admittedly, due to exhaustion on my part).
2. Compared to the previous instance three years ago (in Utah), the main themes have significantly changed. Scalability, approximate methods, non-asymptotic results, 1/n methods … these keywords are now on everyone’s lips. Can’t wait to see if MCQMC’14 will feel that different from MCQMC’12.
3. The community is rightfully concerned about scaling Monte Carlo methods to big data, with some people pointing out that models should also be rethought in this new context.
4. The place of software developers in the conference, or simply references to software packages in the talks, is much greater than it used to be. It’s a very good sign towards reproducible research in our field. There’s still a lot of work to do, in particular in terms of making parallel computing easier to access (time to advertise LibBi a little bit). On a related note, many people now point out whether their proposed algorithms are parallel-friendly or not.
5. Going from the Rockies to the Alps, the food drastically changed from cheeseburgers to just melted cheese. Bread could be found but ground beef and Budweiser were reported missing.
6. It’s fun to have an international conference in your home country, but switching from French to English all the time was confusing.

Back in flooded Oxford now!

## Joint Statistical Meeting 2013

Posted in General, Seminar/Conference, Statistics by Pierre Jacob on 23 July 2013

A typical statistical meeting.

Hey,

In a few weeks (August 3-8) I’ll attend the Joint Statistical Meeting in Montréal, Canada. According to Wikipedia it’s been held every year since 1840 and now gathers more than 5,000 participants!

I’ll talk in a session organized by Scott Schmidler, entitled Adaptive Monte Carlo Methods for Bayesian Computation; you can find the session programme here [online program]. I’ll talk about score and Fisher observation matrix estimation in state-space models.

According to the rumour and Christian’s reflections on the past years (2009, 2010, 2011), I should prepare my schedule in advance to really enjoy this giant meeting. So if you want to meet there, please send me an e-mail!

See you in Montréal!

## Back from ISBA Regional Meeting in India

Posted in Seminar/Conference, Statistics by Pierre Jacob on 15 January 2013

Hello everyone,

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.

## A glimps of Inverse Problems

Posted in General, Seminar/Conference, Statistics by JB Salomond on 15 November 2012

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

$Y_i = K\mu + \frac{1}{\sqrt{n}} Z$
where $K$ is a linear operator and $Z$ 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 $f$ toward a true distribution $f_0$  given the data, with respect to some distance $d(.,.)$,  well known problem is to derive concentration rates, that is the rate $\epsilon_n$ such that

$\pi(d(f,f_0) > \epsilon_n | X^n) \to 0.$

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 !