continuous herded Gibbs sampling

Read a short paper by Laura Wolf and Marcus Baum on Gibbs herding, where herding is a technique of “deterministic sampling”, for instance selecting points over the support of the distribution by matching exact and empirical (or “empirical”!) moments. Which reminds me of the principal points devised by my late friend Bernhard Flury. With an unclear argument as to why it could take over random sampling:

“random numbers are often generated by pseudo-random number generators, hence are not truly random”

Especially since the aim is to “draw samples from continuous multivariate probability densities.” The sequential construction of such a sample proceeds sequentially by adding a new (T+1)-th point to the existing sample of y’s by maximising in x the discrepancy

(T+1)\mathbb E^Y[k(x,Y)]-\sum_{t=1}^T k(x,y_t)

where k(·,·) is a kernel, e.g. a Gaussian density. Hence a complexity that grows as O(T). The current paper suggests using Gibbs “sampling” to update one component of x at a time. Using the conditional version of the above discrepancy. Making the complexity grow as O(dT) in d dimensions.

I remain puzzled by the whole thing as these samples cannot be used as regular random or quasi-random samples. And in particular do not produce unbiased estimators of anything. Obviously. The production of such samples being furthermore computationally costly it is also unclear to me that they could even be used for quick & dirty approximations of a target sample.

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