We develop a family of upper and lower bounds on the worst-case expected KL loss for estimating a discrete distribution on a finite number m of points, given N i.i.d. samples. Our...
As real-world Bayesian networks continue to grow larger and more complex, it is important to investigate the possibilities for improving the performance of existing algorithms of ...
We develop a normative theory of interaction-negotiation in particular--among self-interested computationally limited agents where computational actions are game-theoretically tre...
Recent research in decision theoretic planning has focussedon making the solution of Markov decision processes (MDPs) more feasible. We develop a family of algorithms for structur...
Craig Boutilier, Ronen I. Brafman, Christopher W. ...
Recursive graphical models usually underlie the statistical modelling concerning probabilistic expert systems based on Bayesian networks. This paper de nes a version of these mode...