Confronting intractability in Bristol

Here are the (revised) slides of my talk this afternoon at the Confronting Intractability in Statistical Inference workshop in Bristol, supported by SuSTain. The novelty is in the final part, where we managed to apply our result to a three population genetic escenario using one versus two δμ summary statistics. This should be the central new example in the incoming revision of our paper to Series B.

More generally, the meeting is very interesting, with great talks and highly relevant topics: e.g., yesterday, I finally understood what transportation models meant (at the general level) and how they related to copula modelling, saw a possible connection from computer models to ABC, got inspiration to mix Gaussian processes with simulation output, and listened to the whole exposition of Simon Wood’s alternative to ABC (much more informative than the four pages of his paper in Nature!). Despite (or due to?) sampling Bath ales yesterday night, I even woke up early enough this morning to run over and under the Clifton suspension bridge, with a slight drizzle that could not really be characterized as rain…

7 Responses to “Confronting intractability in Bristol”

  1. […] about the relevance of a given summary statistics, as already explained in the talks I presented in Bristol and Glasgow. We also ran a realistic (and, I think, illuminating!) experiment to assess the impact […]

  2. […] the overlap with the previous meetings in Bristol and in Banff was again limited: Arnaud Doucet rewrote his talk towards less technicity, which means […]

  3. […] this is the second meeting on computational statistics in a row for me and several other participants! Now in Edinburgh, in […]

  4. […] day of a great workshop! I filled more pages of my black notebook (“bloc”) than in the past month!!! This […]

  5. […] Xi'an's Og an attempt at bloggin, from scratch… « Confronting intractability in Bristol […]

  6. Just as a passing comment, we examined a hydrological computer model via ABC in our recent marginal-adjustment/Bayes-linear-analysis paper.

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