likelihood-free nested sampling

Last week, I came by chance across a paper by Jan Mikelson and Mustafa Khammash on a likelihood-free version of nested sampling (a popular keyword on the ‘Og!). Published in 2020 in PLoS Comput Biol. The setup is a parameterised and hidden state-space model, which allows for an approximation of the (observed) likelihood function L(θ|y) by means of a particle filter. An immediate issue with this proposal is that a novel  filter need be produced for a new value of the parameter θ, which makes it enormously expensive. It then gets more bizarre as the [Monte Carlo] distribution of the particle filter approximation ô(θ|y) is agglomerated with the original prior π(θ) as a joint “prior” [despite depending on the observed y] and a nested sampling is conducted with level sets of the form

ô(θ|y)>ε.

Actually, if the Monte Carlo error was null, that is, if the number of particles was infinite,

ô(θ|y)=L(θ|y)

implies that this is indeed the original nested sampler. Simulation from the restricted region is done by constructing an extra density estimator of the constrained distribution (in θ)…

“We have shown how using a Monte Carlo estimate over the livepoints not only results in an unbiased estimator of the Bayesian evidence Z, but also allows us to derive a formulation for a lower bound on the achievable variance in each iteration (…)”

As shown by the above the authors insist on the unbiasedness of the particle approximation, but since nested sampling is not producing an unbiased estimator of the evidence Z, the point is somewhat moot. (I am also rather surprised by the reported lack of computing time benefit in running ABC-SMC.)

PLoS topic page on ABC

A few more comments on the specific entry on ABC written by Mikael Sunnåker et al…. The entry starts with the representation of the posterior probability of an hypothesis, rather than with the posterior density of a model parameter, which seems to lead the novice reader astray. After all, (a) ABC was not introduced for conducting model choice and (b) interchanging hypothesis and model means that the probability of an hypothesis H as used in the entry is actually the evidence in favour of the corresponding model. (There are a few typos and grammar mistakes, but I assume either PLoS or later contributors will correct those.) When the authors state that the “outcome of the ABC rejection algorithm is a set of parameter estimates distributed according to the desired posterior distribution”, I think they are misleading the readers as they forget the “approximative” aspect of this distribution. Further below, I would have used the title “Insufficient summary statistics” rather than “Sufficient summary statistics”, as it spells out more clearly the fundamental issue with the potential difficulty in using ABC. (And I am not sure the subsequent paragraph on “Choice and sufficiency of summary statistics” should bother with the sufficiency aspects… It seems to me much more relevant to assess the impact on predictive performances.)

Although this is most minor, I would not have made mention of the (rather artificial) “table for interpretation of the strength in values of the Bayes factor (…) originally published by Harold Jeffreys^[6] “. I obviously appreciate very much that the authors advertise our warning about the potential lack of validity of an ABC based Bayes factor! I also like the notion of “quality control”, even though it should only appear once. And the pseudo-example is quite fine as an introduction, while it could be supplemented with the outcome resulting from a large n, to be compared with the true posterior distribution. The section “Pitfalls and remedies” is remarkable in that it details the necessary steps for validating a ABC implementation: the only entry I would remove is the one about “Prior distribution and parameter ranges”, in that this is not a problem inherent to ABC… (Granted, the authors present this as a “general risks in statistical inference exacerbated in ABC”, which makes more sense!) It may be that the section on the non-zero tolerance should emphasize more clearly the fact that ε should not be zero. As discussed in the recent Read Paper by Fearnhead and Prangle when envisioning ABC as a non-parametric method of inference.

At last, it is always possible to criticise the coverage of the historical part, since ABC is such a recent field that it is constantly evolving. But the authors correctly point out to (Don) Rubin on the one hand and to Diggle and Graton on the other. Now, I would suggest adding in this section links to the relevant softwares like our own DIY-ABC…

(Those comments have also been posted on the PLoS Computational Biology wiki.)

PLoS computational biology meets wikipedia

Robin Ryder pointed out to me this new experiment run by PLoS since March 2012, namely the introduction of a new article type, “called “Topic Pages” and written in the style of a Wikipedia article“. Not only this terrific idea gives more credence to Wikipedia biology pages, at least in their early stage, but also “the paper contains direct links to Wikipedia pages for background“. Now note that PLoS keeps a wiki separate from Wikipedia. I wonder about the development of a similar interface for statistics, maybe as a renaissance of the former StatProb wiki initiated by John Kimmel two years ago. And mostly abandoned for the past months…

When looking around the site I came upon a page on ABC written by Mikael Sunnåker et al.! A very nice survey of the existing debates around ABC, including uncertainties on the validity of the ABC approximation to the Bayes factor. Ad mentioning the original version of Donald Rubin (1984, AoS). As well as of Peter Diggle and Richard Gratton (1984, JRSS Series B). (I have a lingering feeling I may have seen this paper earlier as a referee and that I sadly missed the connection with this wiki page, hence refereed it as a “classical” submission… However, I just cannot remember whether or not this happened, nor can I find any trace in my past reviews! Which may hint at a weakness of this solution, by the way, namely that referees are less eager to review surveys than novel research articles…) To reinforce the above point, compare this page on ABC with the page on ABC produced by Wikipedia!