Konferenční příspěvek

Vomlelová M., Vomlel J.

Proceedings of Czech-Japan Seminar on Data Analysis and Decision Making under Uncertainty

Universitatis Ostraviensis

Ostrava

2009

Ing. Jiří Vomlel, Ph.D. (ÚTIA - Oddělení matematické teorie rozhodování)

Bayesian network, probabilistic inference, tensor rank-one d

We use the computer game of Minesweeper to illustrate few modeling tricks utilized when applying Bayesian network (BN) models in real applications. Among others, we apply rank-one decomposition (ROD) toconditional probability tables (CPTs) representing addition. Typically, this transformation helps to reduce the computational complexity of probabilistic inference with the BN model. However, in this paper we will see that (except for the total sum node) when ROD is applied to the whole CPT it does not bring any savings for the BN model of Minesweeper. Actually, in order to gain from ROD we need minimal rank-one decompositions of CPTs when the state of the dependent variable is observed. But this is not known and it is a topic for our future research.