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Here are comments by Olli following my post:

I think we found a general means to obtain accurate ABC in the sense of matching the posterior mean or MAP exactly, and then minimising the KL distance between the true posterior and its ABC approximation subject to this condition. The construction works on an auxiliary probability space, much like indirect inference. Now, we construct this probability space empirically, this is where our approach differs first from indirect inference and this is where we need the “summary values” (>1 data points on a summary level; see Figure 1 for clarification). Without replication, we cannot model the distribution of summary values but doing so is essential to construct this space. Now, lets focus on the auxiliary space. We can fiddle with the tolerances (on a population level) and m so that on this space, the ABC approximation has the aforesaid properties. All the heavy technical work is in this part. Intuitively, as m increases, the power increases for sufficiently regular tests (see Figure 2) and consequently, for calibrated tolerances, the ABC approximation on the auxiliary space goes tighter. This offsets the broadening effect of the tolerances, so having non-identical lower and upper tolerances is fine and does not hurt the approximation. Now, we need to transport the close-to-exact ABC approximation on the auxiliary space back to the original space. We need some assumptions here, and given our time series example, it seems these are not unreasonable. We can reconstruct the link between the auxiliary space and the original parameter space as we accept/reject. This helps us understand (with the videos!) the behaviour of the transformation and to judge if its properties satisfy the assumptions of Theorems 2-4. While we offer some tools to understand the behaviour of the link function, yes, we think more work could be done here to improve on our first attempt to accurate ABC.

“The paper also insists over and over on sufficiency, which I fear is a lost cause.” To clarify, all we say is that on the simple auxiliary space, sufficient summaries are easily found. For example, if the summary values are normally distributed, the sample mean and the sample variance are sufficient statistics. Of course, this is not the original parameter space and we only transform the sufficiency problem into a change of variable problem. This is why we think that inspecting and understanding the link function is important.

“Another worry is that the … test(s) rel(y) on an elaborate calibration”. We provide some code here  for everyone to try out. In our examples, this did not slow down ABC considerably. We generally suppose that the distribution of the summary values is simple, like Gaussian, Exponential, Gamma, ChiSquare, Lognormal. In these cases, the ABC approximation takes on an easy-enough-to-calibrate-fast functional form on the auxiliary space.

“This Theorem 3 sounds fantastic but makes me uneasy: unbiasedness is  a sparse property that is rarely found in statistical problems. … Witness the use of “essentially unbiased” in Fig. 4.” What Theorem 3 says is that if unbiasedness can be achieved on the simple auxiliary space, then there are regularity conditions under which these properties can be transported back to the original parameter space. We hope to illustrate these conditions with our examples, and to show that they hold in quite general cases such as the time series application. The thing in Figure 4 is that the sample autocorrelation is not an unbiased estimator of the population autocorrelation. So unbiasedness does not quite hold on the auxiliary space and the conditions of Theorem 3 are not satisfied. Nevertheless, we found this bias to be rather negligible in our example and the bigger concern was the effect of the link function.

And here are Olli’s slides:

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