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Experiences with Modelling Issues in Building Probabilistic Networks [chapter]

Linda C. van der Gaag, Eveline M. Helsper
2002 Lecture Notes in Computer Science  
Also, literature on network-specific modelling issues is quite scarce.  ...  As we have developed a large probabilistic network for a complex medical domain, we have encountered and resolved numerous non-trivial modelling issues.  ...  We are grateful to Babs Taal and Berthe Aleman from the Netherlands Cancer Institute, Antoni van Leeuwenhoekhuis, who provided the domain knowledge for the construction of the oesophagus network.  ... 
doi:10.1007/3-540-45810-7_4 fatcat:ry3yndudsfgc3k5qpbcc6lnd2e

Comparison of Rule-Based and Bayesian Network Approaches in Medical Diagnostic Systems [chapter]

Agnieszka Oniésko, Peter Lucas, Marek J. Druzdzel
2001 Lecture Notes in Computer Science  
We describe a study in which we compare rule-based systems to systems based on Bayesian networks.  ...  Almost two decades after the introduction of probabilistic expert systems, their theoretical status, practical use, and experiences are matching those of rule-based expert systems.  ...  Introduction Two major classes of expert systems are those based on rules, known as rulebased expert systems, and those based on probabilistic graphical models, often referred to as probabilistic expert  ... 
doi:10.1007/3-540-48229-6_40 fatcat:smg7hj5qtzeidkybvnckng55iu

A SMILE web-based interface for learning the causal structure and performing a diagnosis of a Bayesian network

Nipat Jongsawat, Wichian Premchaiswadi
2009 2009 IEEE International Conference on Systems, Man and Cybernetics  
Bayesian networks (BNs) are probabilistic graphical models that are widely used for building diagnosis-and decision-support expert systems.  ...  Learning the structure of a Bayesian network model and causal relations from a dataset or database is important for large BNs analysis.  ...  ACKNOWLEDGMENT The authors would like to thank the Decision Systems Laboratory, University of Pittsburgh for supporting documents, and source file of the engines: Structural Modeling, Inference, and Learning  ... 
doi:10.1109/icsmc.2009.5346198 dblp:conf/smc/JongsawatP09a fatcat:q5ncofq5n5crlgajgmh734wrhi

Converting a rule-based expert system into a belief network

M. Korver, P. J. F. Lucas
1993 Medical Informatics  
In contrast with the heuristic techniques for reasoning with uncertainty employed in many rule-based expert systems, the theory of belief networks is mathematically sound, based on techniques from probability  ...  In this article, we discuss the design of a belief network reformulation of the diagnostic rule-based expert system HEPAR.  ...  On the one hand, assistance in medical diagnosis can be offered by expert systems employing heuristic reasoning models, based on the experience of clinicians in the management of their patients.  ... 
doi:10.3109/14639239309025312 pmid:8289533 fatcat:xzw4peilmvaarjtvzwzz6ywsgi

Major Approaches to Medical Diagnosis and their Drawbacks

2009 Analele Universităţii "Dunărea de Jos" Galaţi: Fascicula III, Electrotehnică, Electronică, Automatică, Informatică  
The ability to reason within a dynamical environment is of a crucial importance in Artificial Intelligence.  ...  Medical diagnosis is a dynamic and very complex field which needs special attention Our quest is for a system for medical diagnosis, that could model its search space efficiently and dynamically, while  ...  Firstly, it focuses on a sub-part of the medical model by selective, rule-based activation of hypotheses.  ... 
doaj:905134691bea4c138e6de57ecc626f8b fatcat:tve3gtzmineo3cpy6lpem2vysy

Probabilistic asthma case finding: a noisy or reformulation

Vibha Anand, Stephen M Downs
2008 AMIA Annual Symposium Proceedings  
Bayesian Networks are used to model domain knowledge with natural perception of causal influences.  ...  In this paper we report the results of an empirical study in the domain of asthma case finding that compares the Noisy-OR reformulation of the expert BN with the expert BN trained using large clinical  ...  Development of the CHICA system was supported by grants from NLM (1 K22 LM009160-01), AHRQ -04-0015, and MCHB (U22MC06969)  ... 
pmid:18998893 pmcid:PMC2656011 fatcat:vdszuwjt2rbeponxu7y7ekausa

Compatible and incompatible abstractions in Bayesian networks

Barbaros Yet, D. William R. Marsh
2014 Knowledge-Based Systems  
Abstraction Knowledge-based models Graphical probabilistic models a b s t r a c t The graphical structure of a Bayesian network (BN) makes it a technology well-suited for developing decision support models  ...  Some of these steps introduce approximations, which can be identified from changes in the set of conditional independence (CI) assertions of a network.  ...  The systems engineering approach uses network fragments [7] as basic elements of model building.  ... 
doi:10.1016/j.knosys.2014.02.020 fatcat:laz3dabbgjfnhiziy64dys5mni

Applicability of Data Mining Technique Using Bayesians Network in Diagnosis of Genetic Diseases

Hugo Pereira
2013 International Journal of Advanced Computer Science and Applications  
So, it has been used classification techniques based in decision trees, probabilistic networks (Naïve Bayes, TAN e BAN) and neural MLP network (Multi-Layer Perception) and training algorithm by error retro-propagation  ...  Described tools capable of propagating evidence and developing techniques of generating efficient inference techniques to combine expert knowledge with data defined in a database.  ...  The problem encountered in building the Bayesian network and the forming of an expert probabilistic system was to obtain knowledge, in that, most data which served as a parameter for obtaining the results  ... 
doi:10.14569/ijacsa.2013.040107 fatcat:4ftmqtydyfe5hfzrziog3yd5uu

A Probabilistic Software Risk Assessment and Estimation Model for Software Projects

Chandan Kumar, Dilip Kumar Yadav
2015 Procedia Computer Science  
In this paper, a probabilistic software risk estimation model is proposed using Bayesian Belief Network (BBN) that focuses on the top software risk indicators for risk assessment in software development  ...  In order to assess the constructed model, an empirical experiment has been performed, based on the data collected from software development projects used by an organization.  ...  Nomenclature RS Bayesian Belief Network A Bayesian belief network (BBN) models the causal relationships of a system or dataset and provides a graphical representation of this causal structure through  ... 
doi:10.1016/j.procs.2015.06.041 fatcat:ymlo5fv6qfcnflubgynv6pavt4

A quantitative assessment model for IT system solution selection with Bayesian network for enterprise architecture decisions

Pinar Yildiran, Huseyin Selcuk Kilic, Bahar Sennaroglu
2018 Pressacademia  
Bayesian Network which is built from causal maps, is used to develop a proposed framework on scenario-based assessment of ICT System Solution in terms of EA and EE principles and Probabilistic CONCLUSION  ...  Causal Maps are useful for building Bayesian Networks. Bayesian (or belief) networks show graphically probabilistic relationship between a set of variables.  ... 
doi:10.17261/pressacademia.2018.897 fatcat:vimotmulkjfuvckj6ou5jr6wfq

Probabilistic graphic models applied to identification of diseases

Renato Cesar Sato, Graziela Tiemy Kajita Sato
2015 Einstein (São Paulo)  
In this text, we present basic use of probabilistic graphic models as tools to analyze causality in health conditions.  ...  The broad dissemination of computed systems and databases allows systematization of part of decisions through artificial intelligence.  ...  Personalized medicine involves the prediction of disease progression based on interpretation of patient data in the light of a disease model (2) and is one potential area of application of these networks  ... 
doi:10.1590/s1679-45082015rb3121 pmid:26154555 pmcid:PMC4943832 fatcat:wcuolty36nd6njrfo5buusbifm

Probabilistic Risk Assessment for Security Requirements: A Preliminary Study

Seok-Won Lee
2011 2011 Fifth International Conference on Secure Software Integration and Reliability Improvement  
based on the different level of domain expertise.  ...  In this paper, we propose a method for a probabilistic modeldriven risk assessment on security requirements.  ...  Risk is the asset-based risk, it presents the risk extent of an asset can impact on the whole system. We extract the causal relationship and construct the BBN based on the Risk Model in Figure 1 .  ... 
doi:10.1109/ssiri.2011.12 dblp:conf/ssiri/Lee11 fatcat:u5d4ea25pfbw5pgdgp6znrsini

Dealing with uncertainty in Decision Support Systems: Recent trends (2000–2011)

Luis C. Dias, Carlos Henggeler Antunes, David Rios Insua, Luis Cândido Dias, Carlos Henggeler Antunes, David Rios Ínsua
2012 International Journal of Intelligent Decision Technologies  
This paper reviews research in relation with modelling uncertainty within Decision Support Systems (DSS) from 2000 to 2011.  ...  It specifically addresses software that has been built or prototyped with the purpose of supporting actual decision making, which is able to explicitly deal with uncertainty (widely understood) on the  ...  Complex probability models may be sometimes described through graphical models, of which the most popular ones are Bayesian networks, which are also sometimes called causal networks, belief nets or probabilistic  ... 
doi:10.3233/idt-2012-0141 fatcat:a3x5mggnmjgdjci7h2kaiigpxa

Decision Analysis and Expert Systems

Max Henrion, John S. Breese, Eric Horvitz
1991 The AI Magazine  
MYCIN and PROSPECTOR originally intended their schemes as approximations to the probabilistic ideal, which they saw as unattainable . . .  ...  Our focus here is specifically on their contributions to knowledge-based expert systems.  ...  A number of diagnostic systems based on probabilistic and decision-analytic ideas have achieved expert system status in that they have achieved expert-level performance.  ... 
doi:10.1609/aimag.v12i4.919 dblp:journals/aim/HenrionBH91 fatcat:ylql6qmbbbcgzhlbpf6kesvwlm

BN Applications in Operational Risk Analysis: Scope, Limitations and Methodological Requirements [chapter]

Paolo Trucco, Maria Chiara
2012 Bayesian Networks  
Modelling local dependencies in facts amounts to specification of the probabilistic dependence of one variable on other variables.  ...  However, as briefly discussed in Section 4, the specification of the structure of a BBN is often subject to debate because based on expert assumptions and/or on theoretical modelling of the reality under  ...  A Bayesian network is a graphical model that encodes probabilistic relationships among variables of interest.  ... 
doi:10.5772/38858 fatcat:nzrhnjkqnfhyvfvepmywemolfi
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