Part of Advances in Neural Information Processing Systems 7 (NIPS 1994)
Gerhard Paass, Jörg Kindermann
If data collection is costly, there is much to be gained by actively se(cid:173) lecting particularly informative data points in a sequential way. In a Bayesian decision-theoretic framework we develop a query selec(cid:173) tion criterion which explicitly takes into account the intended use of the model predictions. By Markov Chain Monte Carlo methods the necessary quantities can be approximated to a desired preci(cid:173) sion. As the number of data points grows, the model complexity is modified by a Bayesian model selection strategy. The proper(cid:173) ties of two versions of the criterion ate demonstrated in numerical experiments.