## ABC as knn…

**G**érard Biau, Frédéric Cérou, and Arnaud Guyader recently posted an arXiv paper on the foundations of ABC, entitled “New insights into Approximate Bayesian Computation“. They also submitted it to several statistics journals, with no success so far, and I find this rather surprising. Indeed, the paper analyses the ABC algorithm the way it is truly implemented (as in DIYABC for instance), i.e. with a tolerance bound ε that is determined as a quantile of the simulated distances, say the 10% or the 1% quantile. This means in particular that the interpretation of ε as a non-parametric bandwidth, while interesting and prevalent in the literature (see, e.g., Fearnhead and Prangle’s discussion paper), is only an approximation of the actual practice.

**T**he authors of this new paper focus on the mathematical foundations of this practice, by (re)analysing ABC as a k-nearest neighbour (knn) method. Using generic knn results, they thus derive a consistency property for the ABC algorithm by imposing some constraints upon the rate of decrease of the quantile as a function of n. (The setting is restricted to the use of sufficient statistics or, equivalently, to a distance over the whole sample. The issue of summary statistics is not addressed by the paper.) The paper also contains a perfectly rigorous proof (the first one?) of the convergence of ABC when the tolerance ε goes to zero. The mean integrated square error consistency of the conditional kernel density estimate is established for a generic kernel (under usual assumptions). Further assumptions (on the target and on the kernel) allow the authors to obtain precise convergence rates (as a power of the sample size), derived from classical k-nearest neighbour regression, like

in dimensions m larger than 4…. The paper is completely theoretical and highly mathematical (with 25 pages of proofs!), which may explain why it did not meet with success with editors and/or referees, however I definitely think (an abridged version of) this work clearly deserves publication in a top statistics journal as a reference for the justification of ABC! The authors also mention future work in that direction: I would strongly suggest they consider the case of the insufficient summary statistics from this knn perspective.

September 14, 2012 at 9:31 pm

Neat, if you can efficiently search Andrew G’s blog, I proposed Bayes as inherently being a k-nearest neighbour procedure in 2005/6. I had, in a pinch, come up with (what Don Rubin used in his 1984 paper) a two stage sampling model in order to explain Bayes to Epi students.

Not that I did anything with it (or could I with computational resources back then), but I remember the clear lack of enthusiam to see it that way.

It just seemed wrong or worse too simple!