Prior information, sampling distributions, and the curse of dimensionality

By Andrew Curtis, Anthony Lomax. 2001

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Trans-dimensional inverse problems, model comparison and the evidence (2006, methodology): “As the number of unknowns becomes large all Bayesian sampling techniques tend to suffer from the curse of dimensionality (see Curtis & Lomax 2001, for a discussion), and evidence calculations are likely to become impractical with a sampling technique.”
Optimal elicitation of probabilistic information from experts (2004, background): “Curtis & Lomax (2001) show that in such large hyperspaces intuitive or innate notions of probabilities derived from 1D-3D space no longer hold.”