ABC-SMC redux (2)

The ABC paper entitled “An Adaptive Sequential Monte Carlo Method for Approximate Bayesian Computation” by Pierre Del Moral, Arnaud Doucet and Ajay Jasra and discussed in that earlier post is now available on-line. In addition to this discussion, I note (and regret) here that the paper does not analyse Sisson et al. (2007) besides remarking that the computational cost of the ABC-PRC algorithm is quadratic in the number of particles. Re-reading the paper, I find an important feature there is that the update in the weights is reduced to the ratio of the proportions of surviving particles (and nothing else!), due to the choice of the reversal backward kernel Ln-1 and to the use of an (MCMC) invariant transition forward kernel Kn (which can be a standard Metropolis-Hastings kernel for instance).

5 Responses to “ABC-SMC redux (2)”

  1. […] tolerance sequence is not done in an adaptive way, while it could, given the recent developments of Del Moral et al. and of Drovandri and Pettitt (as well as our even more recent still-un-arXived submission to Stat […]

  2. […] which I thought were addressed by the ABC-SMC algorithm of Pierre Del Moral, Arnaud Doucet and Ajay Jasra [not referenced in the current paper], rhe […]

  3. […] Jean-Michel Marin and Jean-Marie Cornuet), which builds on Del Moral, Doucet and Jasra’s ABC-SMC (and hopefully completed soon to be submitted to Statistics and Computing special ABC issue). […]

  4. […] of the previous particle population, but this is the same idea as in Del Moral et al.’s ABC-SMC. The only palatable methodological difference, as far as I can tell, is that the MCMC steps are […]

  5. […] on the more adaptive [PMC] features of ABC-SMC, as processed in our Biometrika ABC-PMC paper and in Del Moral, Doucet and Jasra. (Again, this is not a criticism in that the paper got published in early 2009.) I think that using […]

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