don’t wear your helmet, you could have a bike accident!

As once in a while reappears the argument that wearing a bike helmet increases one’s chances of a bike accident. In the current case, it is to argue against a French regulation proposal that helmets should be compulsory for all cyclists. Without getting now into the pros and cons of compulsory helmet laws (enforced in Argentina, Australia, and New Zealand, as well as some provinces of Canada), I see little worth in the study cited by Le Monde towards this argument. As the data is poor and poorly analysed. First, there is a significant fraction of cycling accidents when the presence of an helmet is unknown. Second, the fraction of cyclists wearing helmets is based on a yearly survey involving 500 persons in a few major French cities. The conclusion that there are three times more accidents among cyclists wearing helmets than among cyclists not wearing helmets is thus not particularly reliable. Rather than the highly debatable arguments that (a) seeing a cyclist with an helmet would reduce the caution of car or bus drivers, (b) wearing an helmet would reduce the risk aversion of a cyclist, (c) sport-cyclists are mostly wearing helmets but their bikes are not appropriate for cities (!), I would not eliminate [as the authors do] the basic argument that helmeted cyclists are on average traveling longer distances. With a probability of an accident that necessarily  increases with the distance traveled. While people renting on-the-go bikes are usually biking short distances and almost never wear helmets. (For the record, I mostly wear a [bright orange] helmet but sometimes do not when going to the nearby bakery or swimming pool… Each time I had a fall, crash or accident with a car, I was wearing an helmet and I once hit my head or rather the helmet on the ground, with no consequence I am aware of!)

approximate Bayesian inference [survey]

In connection with the special issue of Entropy I mentioned a while ago, Pierre Alquier (formerly of CREST) has written an introduction to the topic of approximate Bayesian inference that is worth advertising (and freely-available as well). Its reference list is particularly relevant. (The deadline for submissions is 21 June,)

computing Bayes 2.0

Our survey paper on “computing Bayes“, written with my friends Gael Martin [who led this project most efficiently!] and David Frazier, has now been revised and resubmitted, the new version being now available on arXiv. Recognising that the entire range of the literature cannot be encompassed within a single review, esp. wrt the theoretical advances made on MCMC, the revised version is more focussed on the approximative solutions (when considering MCMC as “exact”!). As put by one of the referees [which were all very supportive of the paper], “the authors are very brave. To cover in a review paper the computational methods for Bayesian inference is indeed a monumental task and in a way an hopeless one”. This is the opportunity to congratulate Gael on her election to the Academy of Social Sciences of Australia last month. (Along with her colleague from Monash, Rob Hyndman.)

Computing Bayes: Bayesian Computation from 1763 to the 21st Century

Last night, Gael Martin, David Frazier (from Monash U) and myself arXived a survey on the history of Bayesian computations. This project started when Gael presented a historical overview of Bayesian computation, then entitled ‘Computing Bayes: Bayesian Computation from 1763 to 2017!’, at ‘Bayes on the Beach’ (Queensland, November, 2017). She then decided to build a survey from the material she had gathered, with her usual dedication and stamina. Asking David and I to join forces and bring additional perspectives on this history. While this is a short and hence necessary incomplete history (of not everything!), it hopefully brings some different threads together in an original enough fashion (as I think there is little overlap with recent surveys I wrote). We welcome comments about aspects we missed, skipped or misrepresented, most obviously!

Rao-Blackwellisation, a review in the making

Recently, I have been contacted by a mainstream statistics journal to write a review of Rao-Blackwellisation techniques in computational statistics, in connection with an issue celebrating C.R. Rao’s 100th birthday. As many many techniques can be interpreted as weak forms of Rao-Blackwellisation, as e.g. all auxiliary variable approaches, I am clearly facing an abundance of riches and would thus welcome suggestions from Og’s readers on the major advances in Monte Carlo methods that can be connected with the Rao-Blackwell-Kolmogorov theorem. (On the personal and anecdotal side, I only met C.R. Rao once, in 1988, when he came for a seminar at Purdue University where I was spending the year.)