  We consider the correlated multiarmed bandit (MAB) problem in which the
rewards associated with each arm are modeled by a multivariate Gaussian random
variable, and we investigate the influence of the assumptions in the Bayesian
prior on the performance of the upper credible limit (UCL) algorithm and a new
correlated UCL algorithm. We rigorously characterize the influence of accuracy,
confidence, and correlation scale in the prior on the decision-making
performance of the algorithms. Our results show how priors and correlation
structure can be leveraged to improve performance.
