## Information Fusion, Causal Probabilistic Network And Probanet II: Inference Algorithms and Probanet System (1997)

Venue: | Proc. 1st Intl. Workshop on Image Analysis and Information Fusion |

Citations: | 2 - 2 self |

### BibTeX

@INPROCEEDINGS{Pan97informationfusion,,

author = {Heping Pan and Daniel McMichael and Marta Lendjel},

title = {Information Fusion, Causal Probabilistic Network And Probanet II: Inference Algorithms and Probanet System},

booktitle = {Proc. 1st Intl. Workshop on Image Analysis and Information Fusion},

year = {1997},

pages = {445--458},

publisher = {}

}

### OpenURL

### Abstract

As an extension of an overview paper [Pan and McMichael, 1997] on information fusion and Causal Probabilistic Networks (CPN), this paper formalizes kernel algorithms for probabilistic inferences upon CPNs. Information fusion is realized through updating joint probabilities of the variables upon the arrival of new evidences or new hypotheses. Kernel algorithms for some dominant methods of inferences are formalized from discontiguous, mathematics-oriented literatures, with gaps lled in with regards to computability and completeness. In particular, possible optimizations on causal tree algorithm, graph triangulation and junction tree algorithm are discussed. Probanet has been designed and developed as a generic shell, or say, mother system for CPN construction and application. The design aspects and current status of Probanet are described. A few directions for research and system development are pointed out, including hierarchical structuring of network, structure decomposition and adaptive inference algorithms. This paper thus has a nature of integration including literature review, algorithm formalization and future perspective.