Druzdzel, M., & Gaag, L. van der. (2000). Building probabilistic networks: Where do the numbers come from? - a guide to the literature (Technical Report No. UU-CS-2000-20).  Home/Search   Document Details and Download   Summary   Related Articles   Check

....planning, monitoring, vision, information retrieval and intelligent tutoring [Conati et al. 1997, Mayo and Mitrovic, 2001, VanLehn and Niu, 2001] Most successful applications to date have been built through knowledge elicitation from experts. In general, this is difficult and time consuming [Druzdzel and van der Gaag, 2001], with problems involving incomplete knowledge of School of Computer Sci. and Soft. Eng. Monash University, VIC 3800, Australia. annn csse.monash.edu.au. y Department of Computer Science, The University of Melbourne, Parkville, VIC 3052, Australia. boneh students.cs.mu.oz.au z As for A. ....

.... System controller Item Answer Item Help Report Answer New game Answers Item type Help Feedback Answer Figure 1: Intelligent Tutoring System Architecture 3 EXPERT ELICITATION It is generally accepted that building a BN for a particular application domain involves three tasks [Druzdzel and van der Gaag, 2001]: 1) identification of the important variables, and their values; 2) identification and representation of the relationships between variables in the network structure; and (3) parameterisation of the network, that is determining the conditional probability tables associated with each network ....

Druzdzel, M. and van der Gaag, L. (2001). Building probabilistic networks: Where do the numbers come from? Guest editors introduction. IEEE Trans. on Knowledge and Data Engineering, 12(4):481--486.

....methods have to be supplemented with modelling techniques from decision analysis and statistics. However, due to the often large size and complex dependence structure of network models, application of the latter techniques is not straightforward in the context of decision theoretic networks [4]. It is also not apparent how knowledge engineering techniques and statistical methods must be combined. Although considerable effort is being spent on developing and The investigations were (partly) supported by the Netherlands Computer Science Research Foundation with financial support from ....

Druzdzel, M., Van der Gaag, L., Henrion, M. and Jensen, F. (eds.): Building Probabilistic Networks: Where Do the Numbers Come From? IJCAI-95 Workshop, Montreal, Quebec, Canada (1995)

....of using non discretized data. 1 Introduction Bayesian belief networks (BNs) sometimes referred to as probabilistic networks, provide a powerful formalism for representing and reasoning under uncertainty. The construction of BNs with domain experts often is a difficult and time consuming task [16]. Knowledge acquisition from experts is difficult because the experts have problems in making their knowledge explicit. Furthermore, it is time consuming because the information needs to be collected manually. On the other hand, databases are becoming increasingly abundant in many areas. By ....

M. Druzdzel, L. C. van der Gaag, M. Henrion, and F. Jensen, editors. Building probabilistic networks: where do the numbers come from?, IJCAI-95 Workshop, Montreal, Qu'ebec, 1995.

....dependencies and independencies between these variables. Their clear semantics make BBNs particularly suitable for being used in tasks such as diagnosis, planning, control, and explanation. Construction of probabilistic networks with domain experts often remains a difficult and time consuming task [12]. Knowledge acquisition from experts is difficult because the experts have problems in making their knowledge explicit. Furthermore, it is time consuming because the information needs to be collected manually. On the other hand, databases are becoming increasingly abundant in many areas. By ....

M. Druzdzel, L. C. van der Gaag, M. Henrion, and F. Jensen, editors. Building probabilistic networks: where do the numbers come from?, IJCAI-95 Workshop, Montreal, Canada, 1995.

....effort, it is generally considered feasible. The assessment of all probabilities required is a much harder task, especially if it has to be performed with the help of human experts. The quantification task is, in fact, often referred to as a major bottleneck in building a probabilistic network [4, 5]. Assessment of the signs for a qualitative probabilistic network tends to require considerably 2 less e#ort from human experts, however [3] Now, by eliciting signs from domain experts for the digraph of a probabilistic network under construction, a qualitative probabilistic network is obtained. ....

M.J. Druzdzel and L.C. van der Gaag, "Building probabilistic networks: where do the numbers come from?" --- Guest editors' introduction, IEEE Transactions on Knowledge and Data Engineering 12 (2000) 481 -- 486. 29

....and the possible e#ects of the di#erent therapies available. We then focused on the elicitation of the probabilities required for the quantitative part of the network. The task of eliciting probabilities is generally acknowledged to be the most daunting in constructing a probabilistic network [Druzdzel Van der Gaag, 2000]. In the domain of oesophageal carcinoma, various sources of probabilistic information appeared to be readily available for the task. However, neither data collection nor a thorough literature review yielded any usable results. The single remaining source of probabilistic information, therefore, ....

M.J. Druzdzel and L.C. van der Gaag (2000). Building probabilistic networks: Where do the numbers come from ? IEEE Transactions on Knowledge and Data Engineering, to appear.

No context found.

Druzdzel, M., & Gaag, L. van der. (2000). Building probabilistic networks: Where do the numbers come from? - a guide to the literature (Technical Report No. UU-CS-2000-20).

No context found.

M. Druzdzel, L. van der Gaag, Building probabilistic networks: Where do the numbers come from? - a guide to the literature, Technical Report UU-CS-2000-20, Institute of Information & Computing Sciences, University of Utrecht, The Netherlands (2000).

No context found.

Druzdzel, M.J. and van der Gaag, L.C., "Building probabilistic networks: Where do the numbers come from?", IEEE Transactions on Knowledge and Data Engineering 12, pp. 481-486.

No context found.

M. Druzdzel, L. van der Gaag, M. Henrion and F. Jensen. Building probabilistic networks: where do the numbers come from. M. Druzdzel, L. C. van der Gaag, M. Henrion, and F. Jensen, editors. Building probabilistic networks: where do the numbers come from?, IJCAI-95 Workshop, Montreal, Canada, 1995.

No context found.

M. Druzdzel, L. van der Gaag, M. Henrion, and F. Jensen. Building probabilistic networks: where do the numbers come from. IEEE Transactions on Knowledge and Data Engineering, 12(4):481 -- 486, 2000.

No context found.

M. Druzdzel, L. van der Gaag, M. Henrion, and F. Jensen. Building probabilistic networks: where do the numbers come from. IEEE Transactions on Knowledge and Data Engineering, 12(4):481 -- 486, 2000.

Online articles have much greater impact   More about CiteSeer.IST   Add search form to your site   Submit documents   Feedback  

CiteSeer.IST - Copyright Penn State and NEC