## A Method for Implementing a Probabilistic Model as a Relational Database (1995)

Venue: | In Eleventh Conference on Uncertainty in Artificial Intelligence |

Citations: | 29 - 19 self |

### BibTeX

@INPROCEEDINGS{Wong95amethod,

author = {S. K. M. Wong and C. J. Butz and Y. Xiang},

title = {A Method for Implementing a Probabilistic Model as a Relational Database},

booktitle = {In Eleventh Conference on Uncertainty in Artificial Intelligence},

year = {1995},

pages = {556--564},

publisher = {Morgan Kaufmann Publishers}

}

### Years of Citing Articles

### OpenURL

### Abstract

This paper discusses a method for implementing a probabilistic inference system based on an extended relational data model.

### Citations

7042 |
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
- Pearl
- 1988
(Show Context)
Citation Context ... be processed as standard relational queries. Conventional database management systems can be easily adopted for implementing such an approximate reasoning system. 1 Introduction Probabilistic models =-=[4, 9, 10]-=- are used for making decisions under uncertainty. The input to a probabilistic model is usually a Bayesian network [10]. It may also consist of a set of potentials which define a Markov network [4]. I... |

1282 |
Local computations with probabilities on graphical structures and their application to expert systems
- Lauritzen, Spiegelhalter
- 1988
(Show Context)
Citation Context ...y also consist of a set of potentials which define a Markov network [4]. In this paper, we assume that the probabilistic model is described by a Markov network. For this model, the propagation method =-=[5, 6, 7, 12, 13]-=- can be conveniently applied to convert the potentials into marginal distributions. There is another important reason to characterize a probabilistic model by a Markov network, as it has been shown th... |

942 | Temporal constraint networks - Dechter, Meiri, et al. - 1991 |

428 |
The Theory of Relational Databases
- Maier
- 1983
(Show Context)
Citation Context ...can be viewed as a relation in the relational database. Furthermore, the database scheme derived from a Markov network forms an acyclic join dependency [15], which possesses many desirable properties =-=[1, 8]-=- in database applications. As the probabilistic model is now represented by a relational data model, a probability request expressed as a conditional probability can be equivalently transformed into a... |

287 |
Bayesian Updating in Causal Probabilistic Networks by Local Computations
- Jensen, Lauritzen, et al.
- 1990
(Show Context)
Citation Context ...y also consist of a set of potentials which define a Markov network [4]. In this paper, we assume that the probabilistic model is described by a Markov network. For this model, the propagation method =-=[5, 6, 7, 12, 13]-=- can be conveniently applied to convert the potentials into marginal distributions. There is another important reason to characterize a probabilistic model by a Markov network, as it has been shown th... |

179 | Nonserial Dynamic Programming - Bertelè, Brioschi - 1972 |

163 |
On the desirability of acyclic database schemes
- Beeri, Fagin, et al.
- 1983
(Show Context)
Citation Context ...can be viewed as a relation in the relational database. Furthermore, the database scheme derived from a Markov network forms an acyclic join dependency [15], which possesses many desirable properties =-=[1, 8]-=- in database applications. As the probabilistic model is now represented by a relational data model, a probability request expressed as a conditional probability can be equivalently transformed into a... |

150 |
Probabilistic Reasoning in Expert Systems
- Neapolitan
- 1990
(Show Context)
Citation Context ... be processed as standard relational queries. Conventional database management systems can be easily adopted for implementing such an approximate reasoning system. 1 Introduction Probabilistic models =-=[4, 9, 10]-=- are used for making decisions under uncertainty. The input to a probabilistic model is usually a Bayesian network [10]. It may also consist of a set of potentials which define a Markov network [4]. I... |

127 | Triangulated graphs and the elimination process - Rose - 1970 |

35 |
Uncertain Information Processing in Expert Systems
- Hájek, Havránek, et al.
- 1992
(Show Context)
Citation Context ... be processed as standard relational queries. Conventional database management systems can be easily adopted for implementing such an approximate reasoning system. 1 Introduction Probabilistic models =-=[4, 9, 10]-=- are used for making decisions under uncertainty. The input to a probabilistic model is usually a Bayesian network [10]. It may also consist of a set of potentials which define a Markov network [4]. I... |

31 |
Junction tree and decomposable hypergraphs
- Jensen
- 1988
(Show Context)
Citation Context ...y also consist of a set of potentials which define a Markov network [4]. In this paper, we assume that the probabilistic model is described by a Markov network. For this model, the propagation method =-=[5, 6, 7, 12, 13]-=- can be conveniently applied to convert the potentials into marginal distributions. There is another important reason to characterize a probabilistic model by a Markov network, as it has been shown th... |

23 | Valuation-based systems for discrete optimization - Shenoy, Shafer - 1990 |

19 |
An axiomatic study of computation in hypertrees
- Shafer
- 1991
(Show Context)
Citation Context |

13 | Representation of Bayesian networks as relational databases
- Wong, Xiang, et al.
- 1994
(Show Context)
Citation Context ...ibutions. There is another important reason to characterize a probabilistic model by a Markov network, as it has been shown that such a network can be represented as a generalized relational database =-=[14, 15, 16]-=-. That is, the probabilistic model can be transformed into an equivalent (extended) relational data model. More specifically, the marginal corresponding to each potential can be viewed as a relation i... |

7 | On axiomatization of probabilistic conditional independence - Wong, Wang - 1994 |

2 |
An extended relational data model for probablistic reasoning
- Wong
- 1994
(Show Context)
Citation Context ...ibutions. There is another important reason to characterize a probabilistic model by a Markov network, as it has been shown that such a network can be represented as a generalized relational database =-=[14, 15, 16]-=-. That is, the probabilistic model can be transformed into an equivalent (extended) relational data model. More specifically, the marginal corresponding to each potential can be viewed as a relation i... |