ABSTRACT
ABSTRACT One of the main motivations to use Bayesian statistical models in a sequential learning environment is to get useful knowledge sooner, and thus derive benefits sooner and/or achieve desired results with less work. A second important motivation is to avoid fitting noise and attempt to get a closer picture of the underlying input/output system operating characteristics — especially when there is limited data.
