## Bayesian sufficiency

“During the past seven decades, an astonishingly large amount of effort and ingenuity has gone into the search fpr resonable answers to this question.”D. Basu

**I**nduced by a vaguely related question on X validated, I re-read Basu’s 1977 great JASA paper on the elimination of nuisance parameters. Besides the limitations of competing definitions of conditional, partial, marginal sufficiency for the parameter of interest, Basu discusses various notions of Bayesian (partial) sufficiency.

“After a long journey through a forest of confusing ideas and examples, we seem to have lost our way.”D. Basu

Starting with Kolmogorov’s idea (published during WW II) to impose to all marginal posteriors on the parameter of interest θ to only depend on a statistic S(x). But having to hold for all priors cancels the notion as the statistic need be sufficient jointly for θ and σ, as shown by Hájek in the early 1960’s. Following this attempt, Raiffa and Schlaifer then introduced a more restricted class of priors, namely where nuisance and interest are a priori independent. In which case a conditional factorisation theorem is a sufficient (!) condition for this Q-sufficiency. But not necessary as shown by the N(θ·σ, 1) counter-example (when σ=±1 and θ>0). *[When the prior on σ is uniform, the absolute average is Q-sufficient but is this a positive feature?]* This choice of prior separation is somewhat perplexing in that it does not hold under reparameterisation.

Basu ends up with three challenges, including the multinomial M(θ·σ,½(1-θ)·(1+σ),½(1+θ)·(1-σ)), with (n¹,n²,n³) as a minimal sufficient statistic. And the joint observation of an Exponential Exp(θ) translated by σ and of an Exponential Exp(σ) translated by -θ, where the prior on σ gets eliminated in the marginal on θ.

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