Mýrin (“The Bog”) is the third novel in the Inspector Erlendur series written by Arnaldur Indridason. It contains the major themes of the series, from the fascination for unexplained disappearances in Iceland to Elendur’s inability to deal with his family responsibilities, to domestic violence, to exhumations. The death that starts the novel takes place in the district of Norðurmýri, “the northern marsh”, not far from the iconic Hallgrimskirkja, and not far either from DeCODE, the genetic company I visited last June and which stores genetic information about close to a million Icelanders, the Íslendingabók. And which plays an important and nefarious role in the current novel. While this episode takes place mostly between Reykjavik and Keflavik, hence does not offer any foray into Icelandic landscapes, it reflects quite vividly on the cultural pressure still present in the recent years to keep rapes and sexual violence a private matter, hidden from an indifferent or worse police force. It also shows how the police misses (in 2001) the important genetic clues for being yet unaware of the immense and frightening possibilities of handling the genetic code of an entire population. (The English and French titles refer to the unauthorised private collections of body part accumulated [in jars] by doctors after autopsies, families being unaware of the fact.) As usual, solving the case is the least important part of the story, which tells about broken lifes and survivors against all odds.
Archive for the Travel Category
While I discussed on the ‘Og in the past the difference I saw between estimating an unknown parameter from a distribution and evaluating a normalising constant, evaluating such constants and hence handling [properly] doubly intractable models is obviously of the utmost importance! For this reason, Nial Friel, Helen Ogden and myself have put together a CRiSM workshop on the topic (with the tongue-in-cheek title of Estimating constants!), to be held at the University of Warwick next April 20-22.
The CRiSM workshop will focus on computational methods for approximating challenging normalising constants found in Monte Carlo, likelihood and Bayesian models. Such methods may be used in a wide range of problems: to compute intractable likelihoods, to find the evidence in Bayesian model selection, and to compute the partition function in Physics. The meeting will bring together different communities working on these related problems, some of which have developed original if little advertised solutions. It will also highlight the novel challenges associated with large data and highly complex models. Besides a dozen invited talks, the schedule will highlight two afternoon poster sessions with speed (2-5mn) oral presentations called ‘Elevator’ talks.
While 2016 is going to be quite busy with all kinds of meetings (MCMSkv, ISBA 2016, the CIRM Statistics month, AISTATS 2016, …), this should be an exciting two-day workshop, given the on-going activity in this area, and I thus suggest interested readers to mark the dates in their diary. I will obviously keep you posted about registration and accommodation when those entries are available.
The BayesComp MCMski V [or MCMskv for short] has now its official website, once again maintained by Merrill Lietchy from Drexel University, Philadelphia, and registration is even open! The call for contributed sessions is now over, while the call for posters remains open until the very end. The novelty from the previous post is that there will be a “Breaking news” [in-between the Late news sessions at JSM and the crash poster talks at machine-learning conferences] session to highlight major advances among poster submissions. And that there will be an opening talk by Steve [the Bayesian] Scott on the 4th, about the frightening prospect of MCMC death!, followed by a round-table and a welcome reception, sponsored by the Swiss Supercomputing Centre. Hence the change in dates. Which still allows for arrivals in Zürich on the January 4th [be with you].
[In the train shuttle at Birmingham airport, two young guys, maybe back from SPA 2015, discussing signal processing:]
– In Bayesian statistics, they use a different approach to testing hypotheses… You see, they put priors on the different hypotheses…
– But in the end it all boils down to concentration inequalities…