Statistics under incomplete knowledge of data

Planning under Incomplete Knowledge

Institut für Informationssysteme, TU Wien A-1040 Wien, Austria feiter,[email protected], fleone,pfeifer,[email protected] Abstract. We propose a new logic-based planning language, called K. Transitions between states of knowledge can be described in K, and the language is well suited for planning under incomplete knowledge. Nonetheless, K also supports the representation of trans...

Learning Bayesian network parameters under incomplete data with domain knowledge

Bayesian networks have gained increasing attention in recent years. One key issue in Bayesian networks (BNs) is parameter learning. When training data is incomplete or sparse or when multiple hidden nodes exist, learning parameters in Bayesian networks (BNs) becomes extremely difficult. Under these circumstances, the learning algorithms are required to operate in a high-dimensional search space...

Extracting Knowledge from Incomplete Data

Decision-makers are often met with situations where optimal decisions have to be made in the presence of missing information. To facilitate such work we propose application of Armstrong axioms. Key–Words: Decision support systems, uncertainty management, inference axioms

Image Reconstruction from Incomplete Data

Image Reconstruction from Incomplete Data