Abstract
This paper describes the method to estimate probabilities on Bayesian belief networks (BNs). A BN has nodes showing random variables and shows cause and effect relationships among nodes as a graph. We calculate posterior probabilities, and then estimate the uncertain plural events. As one of the method for estimating probabilities in BN is stochastic sampling. The method has known as taking time to calculate as BN become larger and more complex. Therefore, this paper proposes a parallel sampling method on BN. Then a BN need efficiently dividing to generate samples in parallel, therefore we use community detection. The number of nodes, which have mutual dependence among nodes, is the least to use community detection. In addition, pipeline processing as generating samples, which have mutual dependence reduces waiting time causing by existing them.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2017 International Symposium on Computer Science and Intelligent Controls, ISCSIC 2017 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 113-117 |
| Number of pages | 5 |
| Volume | 2018-February |
| ISBN (Electronic) | 9781538629413 |
| DOIs | |
| Publication status | Published - 2018 Feb 16 |
| Event | 1st International Symposium on Computer Science and Intelligent Controls, ISCSIC 2017 - Budapest, Hungary Duration: 2017 Oct 20 → 2017 Oct 22 |
Other
| Other | 1st International Symposium on Computer Science and Intelligent Controls, ISCSIC 2017 |
|---|---|
| Country | Hungary |
| City | Budapest |
| Period | 17/10/20 → 17/10/22 |
Fingerprint
Keywords
- Bayesian networks
- dataminig
- parallel sampling
ASJC Scopus subject areas
- Computer Science Applications
- Artificial Intelligence
Cite this
A Parallel Sampling Method for Bayesian Networks. / Kobari, Yoshiki; Kimura, Masaomi.
Proceedings - 2017 International Symposium on Computer Science and Intelligent Controls, ISCSIC 2017. Vol. 2018-February Institute of Electrical and Electronics Engineers Inc., 2018. p. 113-117 8294170.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A Parallel Sampling Method for Bayesian Networks
AU - Kobari, Yoshiki
AU - Kimura, Masaomi
PY - 2018/2/16
Y1 - 2018/2/16
N2 - This paper describes the method to estimate probabilities on Bayesian belief networks (BNs). A BN has nodes showing random variables and shows cause and effect relationships among nodes as a graph. We calculate posterior probabilities, and then estimate the uncertain plural events. As one of the method for estimating probabilities in BN is stochastic sampling. The method has known as taking time to calculate as BN become larger and more complex. Therefore, this paper proposes a parallel sampling method on BN. Then a BN need efficiently dividing to generate samples in parallel, therefore we use community detection. The number of nodes, which have mutual dependence among nodes, is the least to use community detection. In addition, pipeline processing as generating samples, which have mutual dependence reduces waiting time causing by existing them.
AB - This paper describes the method to estimate probabilities on Bayesian belief networks (BNs). A BN has nodes showing random variables and shows cause and effect relationships among nodes as a graph. We calculate posterior probabilities, and then estimate the uncertain plural events. As one of the method for estimating probabilities in BN is stochastic sampling. The method has known as taking time to calculate as BN become larger and more complex. Therefore, this paper proposes a parallel sampling method on BN. Then a BN need efficiently dividing to generate samples in parallel, therefore we use community detection. The number of nodes, which have mutual dependence among nodes, is the least to use community detection. In addition, pipeline processing as generating samples, which have mutual dependence reduces waiting time causing by existing them.
KW - Bayesian networks
KW - dataminig
KW - parallel sampling
UR - http://www.scopus.com/inward/record.url?scp=85052383377&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85052383377&partnerID=8YFLogxK
U2 - 10.1109/ISCSIC.2017.11
DO - 10.1109/ISCSIC.2017.11
M3 - Conference contribution
AN - SCOPUS:85052383377
VL - 2018-February
SP - 113
EP - 117
BT - Proceedings - 2017 International Symposium on Computer Science and Intelligent Controls, ISCSIC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
ER -