Name
Abstract | ||
|---|---|---|
Influence diagrams are probabilistic graphical models used to represent and solve decision problems under uncertainty. Sharp numerical values are required to quantify probabilities and utilities. Yet, real models are based on data streams provided by partially reliable sensors or experts. We propose an interval-valued quantification of these parameters to gain realism in the modelling and to analyse the sensitivity of the inferences with respect to perturbations of the sharp values. An extension of the classical influence diagrams formalism to support interval-valued potentials is provided. Moreover, a variable elimination algorithm especially designed for these models is developed and evaluated in terms of complexity and empirical performances. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-20807-7_49 | Lecture Notes in Artificial Intelligence |
Keywords | Field | DocType |
Influence diagrams,Bayesian networks,Credal networks,Sequential decision making,Imprecise probability | Data stream mining,Decision problem,Variable elimination,Computer science,Imprecise probability,Algorithm,Influence diagram,Bayesian network,Artificial intelligence,Graphical model,Formalism (philosophy),Machine learning | Conference |
Volume | ISSN | Citations |
9161 | 0302-9743 | 1 |
PageRank | References | Authors |
0.37 | 5 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rafael Cabañas | 1 | 16 | 5.09 |
| Alessandro Antonucci | 2 | 189 | 23.31 |
| Andrés Cano | 3 | 193 | 20.06 |
| Manuel Gómez-Olmedo | 4 | 61 | 11.98 |