@CONFERENCE\{IMM2000-04834,
    author       = "L. K. Hansen",
    title        = "Bayesian Averaging is Well-Temperated",
    year         = "2000",
    pages        = "265-271",
    booktitle    = "Advances in Neural Information Processing Systems 1999",
    volume       = "",
    series       = "",
    editor       = "S. Solla et al.",
    publisher    = "{MIT} Press",
    organization = "",
    address      = "",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/4834-full.html",
    abstract     = "Bayesian predictions are stochastic just like predictions of any other
inference scheme that generalize from a finite sample.	 While a simple variational argument shows that Bayes averaging is generalization optimal given that the prior matches the teacher parameter distribution the situation is less clear if the teacher distribution is unknown. I define a class of averaging procedures, the temperated likelihoods, including both Bayes averaging with a uniform prior and maximum likelihood estimation as special cases. I show that
Bayes is generalization optimal in this family for any teacher distribution for two learning problems that are analytically tractable learning the mean of a Gaussian and asymptotics of smooth learners."
}