Entropy Bounds on Bayesian Learning

Olivier Gossner · Tristan Tomala

 


Abstract
An observer of a process (xt) believes the process is governed by Q whereas the true law is P. We bound the expected average distance between P(xt |x1, . . . , xt−1) and Q(xt|x1, . . . , xt−1) for t = 1 . . . n by a function of the relative entropy between the marginals of P and Q on the n first realizations. We apply this bound to the cost of learning in sequential decision problems and to the merging of Q to P.

Keywords
Bayesian Learning · Repeated Decision Problem · Value of Information · Entropy

Mathematics Subject Classification (2000)

62C10 62B10 91A26