ISBA 2026¹
An ISBA World meeting is always an exciting event that verges on the sensory overload! The more when one’s flight is perversely designed to land at my usual bedtime. (I tried waking up very early and running for an hour before catching the plane but this did not make me doze for more than one hour or two in the plane.) To explain why I missed the first two foundation lectures of the day, including the one by my dear friend Sylvia Richardson, as I was still resting (to avoid repeating my fainting upon arrival as happened last year, or later as). Incidentally, these lectures made me realise I had given one in Kyoto (on ABC) in 2012. I however managed to attend David Dunson’s wide recap on Bayesian clustering(s), with slides ChatGPT confused with posters, hence a massive information content had I been able to read them from my seat! More fundamentally, the residual difficulty being the very notion of cluster itself. And a jump back in time with Fumiyasu Komaki going over his corpus of works on the decision theoretic properties of Bayesian predictives when the Kullback-Leibler divergence is the loss function. An interesting feature of his findings is that comparing priors in that sense is equivalent to comparing them for point estimation in Normal and Poisson cases, but not in the Gamma case, for no reason I can fathom. A philosophical point of contention of mine’s was the introduction of two Jeffreys priors when observed data and predicted data are from different distributions (calibrated by the same parameter θ) since it calls for a Lindley’s style criticism that a realisation not (yet) observed modifies the prior distribution on θ. If possibly leading to improved estimation.
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