Show simple item record

dc.contributor.authorYET, B
dc.contributor.authorCONSTANTINOU, AC
dc.contributor.authorFENTON, N
dc.contributor.authorNEIL, M
dc.identifier.citationIEEE Access. (2018). Expected Value of Partial Perfect Information in Hybrid Models using Dynamic Discretization - IEEE Journals & Magazine. [online] Available at: [Accessed 15 Feb. 2018].en_US
dc.description.abstractIn decision theory models, Expected Value of Partial Perfect Information (EVPPI) is an important analysis technique that is used to identify the value of acquiring further information on individual variables. EVPPI can be used to prioritize the parts of a model that should be improved or identify the parts where acquiring additional data or expert knowledge is most beneficial. Calculating EVPPI of continuous variables is challenging, and several sampling and approximation techniques have been proposed. This paper proposes a novel approach for calculating EVPPI in Hybrid Influence Diagram (HID) models (these are Influence Diagrams (IDs) containing both discrete and continuous nodes). The proposed approach transforms the HID into a Hybrid Bayesian Network (HBN) and makes use of the Dynamic Discretization (DD) and the Junction Tree (JT) algorithms to calculate the EVPPI. This is an approximate solution (no feasible exact solution is possible generally for HIDs) but we demonstrate it accurately calculates the EVPPI values. Moreover, unlike the previously proposed simulation-based EVPPI methods, our approach eliminates the requirement of manually determining the sample size and assessing convergence. Hence, it can be used by decision-makers who do not have deep understanding of programming languages and sampling techniques. We compare our approach to the previously proposed techniques based on two case studies.en_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.ispartofIEEE Access
dc.titleExpected Value of Partial Perfect Information in Hybrid Models using Dynamic Discretizationen_US
dc.rights.holder© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
pubs.organisational-group/Queen Mary University of London
pubs.organisational-group/Queen Mary University of London/Faculty of Science & Engineering
pubs.organisational-group/Queen Mary University of London/Faculty of Science & Engineering/Electronic Engineering and Computer Science - Staff

Files in this item


This item appears in the following Collection(s)

Show simple item record

Return to top