Name
Title | ||
|---|---|---|
Inference In Bayesian Networks With Recursive Probability Trees: Data Structure Definition And Operations |
Abstract | ||
|---|---|---|
Recursive probability trees (RPTs) are a data structure for representing several types of potentials involved in probabilistic graphical models. The RPT structure improves the modeling capabilities of previous structures (like probability trees or conditional probability tables). These capabilities can be exploited to gain savings in memory space and/or computation time during inference. This paper describes the modeling capabilities of RPTs as well as how the basic operations required for making inference on Bayesian networks operate on them. The performance of the inference process with RPTs is examined with some experiments using the variable elimination algorithm. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1002/int.21587 | INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS |
Field | DocType | Volume |
Data mining,Data structure,Variable elimination,Conditional probability,Computer science,Inference,Fiducial inference,Bayesian network,Artificial intelligence,Graphical model,Machine learning,Recursion | Journal | 28 |
Issue | ISSN | Citations |
7 | 0884-8173 | 3 |
PageRank | References | Authors |
0.46 | 8 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrés Cano | 1 | 193 | 20.06 |
| Manuel Gómez-Olmedo | 2 | 61 | 11.98 |
| Serafín Moral | 3 | 1218 | 145.79 |
| Cora B. Pérez-Ariza | 4 | 19 | 2.96 |
| Antonio Salmerón | 5 | 595 | 58.71 |