Editing Bayesian Logic Programs

Title: Bayesian Logic Programs

Research Question: How can we create a more flexible and expressive model for representing and reasoning about uncertainty, while maintaining the benefits of both Bayesian networks and logic programming?

Methodology: The authors introduce a new framework called Bayesian Logic Programs. This approach combines the strengths of Bayesian networks and logic programming by establishing a one-to-one mapping between ground atoms and random variables. This allows for a more flexible and expressive model that can represent objects and relations, and handle structured terms and continuous random variables.

Results: The authors show that Bayesian logic programs combine the advantages of both deﬁnite clause logic and Bayesian networks. They also demonstrate that Bayesian logic programs generalize both Bayesian networks and logic programs, allowing for the reuse of ideas developed in both fields.

Implications: The introduction of Bayesian Logic Programs provides a more flexible and expressive model for representing and reasoning about uncertainty. This can lead to improved performance and ease of use in various applications, such as diagnosis, forecasting, automated vision, sensor fusion, and manufacturing control. Furthermore, it can simplify the process of modeling complex systems that involve a variable number of objects or relations, and allow for more efficient updating of models when components are added or deleted.

Link to Article: https://arxiv.org/abs/0111058v1
Authors: 
arXiv ID: 0111058v1

[[Category:Computer Science]]
[[Category:Bayesian]]
[[Category:Logic]]
[[Category:Programs]]
[[Category:Can]]
[[Category:More]]