  Bayesian Optimization is methodology used in statistical modelling that
utilizes a Gaussian process prior distribution to iteratively update a
posterior distribution towards the true distribution of the data. Finding
unbiased informative priors to sample from is challenging and can greatly
influence the outcome on the posterior distribution if only few data are
available. In this paper we propose a novel approach to utilize meta-learning
to automate the estimation of informative conjugate prior distributions given a
distribution class. From this process we generate priors that require only few
data to estimate the shape parameters of the original distribution of the data.

Machine Learning;Artificial Intelligence