  In the need for low assumption inferential methods in infinite-dimensional
settings, Bayesian adaptive estimation via a prior distribution that does not
depend on the regularity of the function to be estimated nor on the sample size
is valuable. We elucidate relationships among the main approaches followed to
design priors for minimax-optimal rate-adaptive estimation meanwhile shedding
light on the underlying ideas.
