|
|
Ockham's Razor from a Fully Probabilistic Design Perspective
Hoffmann, A. ; Quinn, Anthony
This research report investigates an approach to the design of an Ockham prior penalising parametric complexity in the Hierarchical Fully Probabilistic Design (HFPD) [1] setting. We identify a term which penalises the introduction of an additional parameter in the Wold decomposition. We also derive the objective Ockham Parameter Prior (OPI) in this context, based on earlier work [2], and we show that the two are, in fact, closely related. This confers validity on the HFPD Ockham term.
|
|
| |
|
| |
|
|
Variational Bayes in Distributed Fully Probabilistic Decision Making
Šmídl, Václav ; Tichý, Ondřej
We are concerned with design of decentralized control strategy for stochastic systems with global performance measure. It is possible to design optimal centralized control strategy, which often cannot be used in distributed way. The distributed strategy then has to be suboptimal (imperfect) in some sense. In this paper, we propose to optimize the centralized control strategy under the restriction of conditional independence of control inputs of distinct decision makers. Under this optimization, the main theorem for the Fully Probabilistic Design is closely related to that of the well known Variational Bayes estimation method. The resulting algorithm then requires communication between individual decision makers in the form of functions expressing moments of conditional probability densities. This contrasts to the classical Variational Bayes method where the moments are typically numerical.
|
|
|
Automated Preferences Elicitation
Kárný, Miroslav ; Guy, Tatiana Valentine
Systems supporting decision making became almost inevitable in the modern complex world. Their efficiency depends on the sophisticated interfaces enabling a user take advantage of the support while respecting the increasing on-line information and incomplete, dynamically changing user’s preferences. The best decision making support is useless without the proper preference elicitation. The paper proposes a methodology supporting automatic learning of quantitative description of preferences. The proposed elicitation serves to fully probabilistic design, which is an extension of Bayesian decision making.
|
|
| |
guest ::
