For many problems there is only suf£cient prior information for a Bayesian decision maker to identify a class of possible prior distributions. In such cases it is of interest to ...
We generalise the optimisation technique of dynamic programming for discretetime systems with an uncertain gain function. We assume that uncertainty about the gain function is des...
Set-valued estimation offers a way to account for imprecise knowledge of the prior distribution of a Bayesian statistical inference problem. The set-valued Kalman filter, which p...
Kuznetsov’s condition says that variables X and Y are independent when any product of bounded functions f(X) and g(Y) behaves in a certain way: the interval of expected values E...
A generalization of deFinetti’s Fundamental Theorem of Probability facilitates coherent assessment, by iterated natural extension, of imprecise probabilities or expectations, co...