## Projektdetaljer

### Beskrivelse

Large and complex Bayesian networks are usually hard to design. Once designed,

a Bayesian network may need to be adapted to changes in the domain it represents.

We investigate several approaches to facilitating design and maintenance of

Bayesian networks.

Several frameworks have been proposed in the past that ease the specification

of Bayesian networks using object oriented ideas. The group has been working

with so-called object oriented Bayesian networks, and has proposed a framework

that supports a top-down modeling approach.Moreover, we have proposed methods

that exploits the characteristics of object oriented domains when learning the

parameters as well as the structure of a network.

Adaptability is a central concern addressed by the language of relational Bayesian

networks, which we develop, investigate and implement. Relational Bayesian networks

are a logic-based representation language for high-level, adaptable probabilistic

models. These generic models can be instantiated over concrete domains,which leads to a domain-specific model that can then be represented by a Bayesian

network. A simple example of this two-level modeling approach is a general model

of (genetic) inheritance, which can be instantiated over any concrete domain consisting

of members of a given pedigree. The Bayesian networks for the domainspecific

models often become quickly intractable for inference with increasing

size of the domain. As an alternative to Bayesian networks we have investigated

arithmetic circuits as a computational data structure for probabilistic inference in

domain-specific models. Empirical results show that in typical examples we

can handle with arithmetic circuits domains that are about 2-3 times as large as the

largest domains amenable to inference with Bayesian networks.

The language of relational Bayesian networks is implemented in the Primula

system. Key components of Primula are a constructor for standard Bayesian

networks from a relational Bayesian network and a concrete input domain, and an

importance sampling algorithm for approximate inference for model instances not

amenable to exact inference.

a Bayesian network may need to be adapted to changes in the domain it represents.

We investigate several approaches to facilitating design and maintenance of

Bayesian networks.

Several frameworks have been proposed in the past that ease the specification

of Bayesian networks using object oriented ideas. The group has been working

with so-called object oriented Bayesian networks, and has proposed a framework

that supports a top-down modeling approach.Moreover, we have proposed methods

that exploits the characteristics of object oriented domains when learning the

parameters as well as the structure of a network.

Adaptability is a central concern addressed by the language of relational Bayesian

networks, which we develop, investigate and implement. Relational Bayesian networks

are a logic-based representation language for high-level, adaptable probabilistic

models. These generic models can be instantiated over concrete domains,which leads to a domain-specific model that can then be represented by a Bayesian

network. A simple example of this two-level modeling approach is a general model

of (genetic) inheritance, which can be instantiated over any concrete domain consisting

of members of a given pedigree. The Bayesian networks for the domainspecific

models often become quickly intractable for inference with increasing

size of the domain. As an alternative to Bayesian networks we have investigated

arithmetic circuits as a computational data structure for probabilistic inference in

domain-specific models. Empirical results show that in typical examples we

can handle with arithmetic circuits domains that are about 2-3 times as large as the

largest domains amenable to inference with Bayesian networks.

The language of relational Bayesian networks is implemented in the Primula

system. Key components of Primula are a constructor for standard Bayesian

networks from a relational Bayesian network and a concrete input domain, and an

importance sampling algorithm for approximate inference for model instances not

amenable to exact inference.

Status | Igangværende |
---|---|

Effektiv start/slut dato | 19/05/2010 → … |

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