MCMC with multiple tries

Earlier this year, Luca Martino wrote and arXived a review on multiple try MCMC. As its name suggests, the starting point of this algorithm is to propose N potential moves simultaneously instead of one, possibly according to N different proposal (conditional) densities, and to select one by a normalised importance sampling weight. The move is accepted by a Metropolis-Hastings step based on the ratio of the normalisation constants [at the current and at the one-before-current stages]. Besides the cost of computing the summation and generating the different variates, this method also faces the drawback of requiring N-1 supplementary simulations that are only used for achieving detailed balance and computing a backward summation of importance weights. (A first section of the review is dedicated to independent Metropolis-Hastings proposals, q(θ), which make life simpler, but are less realistic in my opinion since some prior knowledge or experimentation is necessary to build a relevant distribution q(θ).) An alternative covered in the survey is ensemble Monte Carlo (Neal, 2011), which produces a whole sample at each iteration, with target the product of the initial targets. This reminded me of our pinball sampler, which aimed at producing a spread-out sample while keeping the marginal correct. Although the motivation sounds closer to a particle sampler. Especially with this associated notion of an empirical approximation of the target. The next part of the review is about delayed rejection, which is a natural alternative approach to speeding up MCMC by considering several possibilities, if sequentially. Started in Antonietta Mira‘s 1999 PhD thesis. The difficulty with this approach is that the acceptance probability gets increasingly complex as the number of delays grows, which may annihilate its appeal relative to simultaneous multiple tries.

5 Responses to “MCMC with multiple tries”

  1. An interesting paper in this area is the unpublished 2004 manuscript by Hakon Tjelmeland, “Using all Metropolis–Hastings proposals to estimate mean values”.

  2. Fabrizio Leisen Says:

    Just a curiosity: why did you choose this picture? :D :D

    • This is a picture of a female All Blacks scoring a neat try. One of multiple tries during that game!!!

      • Fabrizio Says:

        Now, it makes sense! In my young years, the MTM idea hooked me. We wrote a nice paper with Radu and Roberto a few years ago but, as you point out, as sampling method, is not the most efficient one. Although, apparently, some signal processing guys like the MTM and proposed improvements (Luca, for instance).

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