## easy-to-use empirical likelihood ABC

**A** newly arXived paper from a group of researchers at NUS I wish we had discussed when I was there last month. As we wrote this empirical ABCe paper in PNAS with Kerrie Mengersen and Pierre Pudlo in 2012. Plus the SAME paper with Arnaud Doucet and Simon Godsill ten years earlier, which the authors prefer to call data cloning in continuation of the more recent Lele et al. (2007). They could actually have used my original denomination of prior feedback (1992? I remember presenting the idea at Camp Casella in Cornell that summer) as well! Actually, I am not certain invoking prior feedback is quite necessary since this is a form of simulated method of moments as well.

Now, did we really assume that some moments of the distribution were analytically available, although the likelihood was not?! Even before going through the paper, it dawned on me that these theoretical moments could have been simulated instead, since the model is a generative one: for a given parameter value, a direct Monte Carlo approximation to the exact moment can be produced and can serve as a constraint for the empirical likelihood definition. I am surprised and aggrieved that we would not think of this empirical likelihood version of a method of moments. Which is central to the current paper. In the sense that, were the parameter exact, the differences between the moments based on the actual data x⁰ and the moments based on m replicas of the simulated data x¹,x²,… have mean zero, meaning the moment constraint is immediately available. Meaning an empirical likelihood is easily constructed, replacing the actual likelihood in an MCMC scheme, albeit at a rather high computing cost. Congratulations to the authors for uncovering this possibility that we missed!

“The summary statistics in this example were judiciously chosen.”

One point in the paper on which I disagree with the authors is the argument that MCMC sampling based on an empirical likelihood can be seen as an implementation of the pseudo-marginal Metropolis-Hastings method. The major difference in my opinion is that there is no unbiasedness here (and no generic result that indicates convergence to the exact posterior as the number of simulations grows to infinity). The other point unclear to me is about the selection of summaries [or moments] for implementing the method, which seems to be based on their performances in the subsequent estimation, performances that are hard to assess properly in intractable likelihood cases. In the last example of stereological extremes (not covered in our paper), for instance, the output is compared with the parallel synthetic likelihood result.

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