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K.G. Olesen, "Causal Probabilistic Networks with Both Discrete and Continuous Variables," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, no. 3, pp. 275279, March, 1993.  
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@article{ 10.1109/34.204909, author = {K.G. Olesen}, title = {Causal Probabilistic Networks with Both Discrete and Continuous Variables}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {15}, number = {3}, issn = {01628828}, year = {1993}, pages = {275279}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.204909}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE Transactions on Pattern Analysis and Machine Intelligence TI  Causal Probabilistic Networks with Both Discrete and Continuous Variables IS  3 SN  01628828 SP275 EP279 EPD  275279 A1  K.G. Olesen, PY  1993 KW  probabilistic reasoning; discrete variables; causal probabilistic networks; expert system shell; handling uncertainty by general influence networks; HUGIN; continuous variables; linear additive normally distributed variables; knowledge acquisition; belief revision; expert systems; inference mechanisms; knowledge acquisition; uncertainty handling VL  15 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
An extension of the expert system shell known as handling uncertainty by general influence networks (HUGIN) to include continuous variables, in the form of linear additive normally distributed variables, is presented. The theoretical foundation of the method was developed by S.L. Lauritzen, whereas this report primarily focus on implementation aspects. The approach has several advantages over purely discrete systems. It enables a more natural model of of the domain in question, knowledge acquisition is eased, and the complexity of belief revision is most often reduced considerably.
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