## multiple try/point Metropolis algorithm

**A**mong the arXiv documents I printed at the turn of the year in order to get a better look at them (in the métro if nowhere else!), there were two papers by Luca Martino and co-authors from Universidad Carlos III, Madrid, *A multi-point Metropolis scheme with generic weight functions* and *Different acceptance functions for multiple try Metropolis schemes*. The multiple-try algorithm sounds like another version of the delayed sampling algorithm of Tierney and Mira (1999) and Green and Mira (2001). I somehow missed it, even though it was introduced in Liu et al. (2000) and Quin and Liu (2001). Multiple-try Metropolis builds upon the idea that, instead of making one proposal at a time, it is feasible to build a sequence of proposals and to pick one among those, presumably rather likely and hence more open to being accepted. The sequence of proposals may depend upon the past propositions as well as on the current value, lending some degree of adaptability to the scheme. In the current implementation, the algorithm remains rather clumsy [in my opinion] in that (a) a fixed horizon ** N** need be fixed and (b) an additional series of backward simulations need be produced simply to keep the balance equation happy… Hence a total number of simulations O(

**for one possible acceptance. The first note slightly extends Quin and Liu (2001) by using a fairly general weighting scheme. The second paper studies some particular choices for the weights in a much less adaptive scheme (where parallelisation would be an appropriate alternative, since each proposal in the multiple try only depends on the current value of the chain). But it does not demonstrate a more efficient behaviour than when using a cycle or a mixture of Metropolis-Hastings algorithms. The method seems to regain popularity, though, as Roberto Casarin, Radu Craiu and Fabrizio Leisen (also from Carlos III) arXived a paper on a multiple-try algorithm, connected with population Monte Carlo, and more recently published it in Statistics and Computing.**

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January 23, 2012 at 3:31 pm

Radford makes a good point. In our experience it is difficult for a naive implementation of multiple-try Metropolis to achieve an advantage over ordinary Metropolis. As he suggests above, a possible way to overcome this is to avoid computation of the target density at each of the K multiple tries. This is the approach we took here: http://jmlr.csail.mit.edu/proceedings/papers/v9/pandolfi10a/pandolfi10a.pdf (the details are in Section 3.2).

January 23, 2012 at 2:38 am

Indeed, a problem with the original multiple-try Metropolis paper is that in none of the examples given do they demonstrate an advantage over ordinary Metropolis with the same proposal distribution. (They don’t even present such a comparison, preferring to compare against ordinary Metropolis with a different proposal distribution.)

My “Ensemble MCMC” method (see http://arxiv.org/abs/1101.0387, and my blog entry at http://radfordneal.wordpress.com/2011/01/01/ensemble-mcmc/) can be seen as better way of doing something similar. One point I make in that paper is that one can expect an advantage only when there is some computational shortcut for evaluating the density at K points with less than K times the cost of evaluating the density at one point.

January 23, 2012 at 9:28 am

Which is why I think the method should be considered in a parallel environment where every proposal can be recycled towards estimation, in the spirit of our parallel Metropolis-Hastings algorithm paper. (Thanks!)