Name
Abstract | ||
|---|---|---|
The paper shows a method to compute a posteriori intervals of probabilities when the initial conditional information is given also with intervals of probabilities. The right way of doing an exact computation is with the associated convex set of probabilities. Probability trees are used to represent these initial conditional convex sets, because they save enormously the required space. This paper proposes a simulated annealing algorithm, using probability trees as the representation of the convex sets, in order to compute the a posteriori intervals. |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1007/3-540-44652-4_25 | ECSQARU |
Keywords | Field | DocType |
probability tree,initial conditional convex set,associated convex,simulated annealing algorithm,exact computation,probability trees,posteriori interval,simulated annealing,required space,convex set,computing intervals,initial conditional information,initial condition | Extreme point,Conditional probability distribution,Conditional probability,Convex set,Regular conditional probability,Bayesian network,Artificial intelligence,Chain rule (probability),Machine learning,Mathematics,Law of total probability | Conference |
ISBN | Citations | PageRank |
3-540-42464-4 | 0 | 0.34 |
References | Authors | |
16 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrés Cano | 1 | 193 | 20.06 |
| Serafín Moral | 2 | 1218 | 145.79 |