Name
Title | ||
|---|---|---|
Fast Factorisation Of Probabilistic Potentials And Its Application To Approximate Inference In Bayesian Networks |
Abstract | ||
|---|---|---|
We present an efficient procedure for factorising probabilistic potentials represented as probability trees. This new procedure is able to detect some regularities that cannot be captured by existing methods. In cases where an exact decomposition is not achievable, we propose a heuristic way to carry out approximate factorisations guided by a parameter called factorisation degree, which is fast to compute. We show how this parameter can be used to control the tradeoff between complexity and accuracy in approximate inference algorithms for Bayesian networks. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1142/S0218488512500110 | INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS |
Keywords | Field | DocType |
Bayesian networks, probability trees, factorisation, probabilistic inference | Probabilistic inference,Heuristic,Algorithm,Approximate inference,Bayesian network,Factorization,Artificial intelligence,Probabilistic logic,Mathematics,Machine learning,Bayesian probability | Journal |
Volume | Issue | ISSN |
20 | 2 | 0218-4885 |
Citations | PageRank | References |
3 | 0.43 | 20 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrés Cano | 1 | 193 | 20.06 |
| Manuel Gómez-Olmedo | 2 | 61 | 11.98 |
| Cora B. Pérez-Ariza | 3 | 19 | 2.96 |
| Antonio Salmerón | 4 | 595 | 58.71 |