Integrating Data Sources and Network Analysis Tools to Support the Fight Against Organized Crime
PAISI, PACCF and SOCO '08 Proceedings of the IEEE ISI 2008 PAISI, PACCF, and SOCO international workshops on Intelligence and Security Informatics
Information Sciences: an International Journal
A simple graphical approach for understanding probabilistic inference in Bayesian networks
Information Sciences: an International Journal
Factors influencing the performance of Dynamic Decision Network for INQPRO
Computers & Education
Context-aware handoff decision for wireless access networks using Bayesian networks
Proceedings of the 2009 Annual Research Conference of the South African Institute of Computer Scientists and Information Technologists
A formal comparison of variable elimination and arc reversal in Bayesian network inference
Intelligent Decision Technologies
Adding diagnostics to intelligent robot systems
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Adaptive modelling for adaptive water quality management in the Great Barrier Reef region, Australia
Environmental Modelling & Software
Optimal dynamic decision network model for scientific inquiry learning environment
Applied Intelligence
Environmental Modelling & Software
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation - Volume Part I
Using four cost measures to determine arc reversal orderings
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
SAIFA: A web-based system for Integrated Production of olive cultivation
Computers and Electronics in Agriculture
Development of an internet based system for modeling biotin metabolism using Bayesian networks
Computer Methods and Programs in Biomedicine
Evaluating probabilistic inference techniques: a question of "When," not "Which"
SUM'11 Proceedings of the 5th international conference on Scalable uncertainty management
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
A multi-modal emotion recognition system for persistent and non-invasive personal health monitoring
Proceedings of the 2nd Conference on Wireless Health
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
Properties of Bayesian student model for INQPRO
Applied Intelligence
Exploiting the probability of observation for efficient bayesian network inference
Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
Value of information and mobility constraints for sampling with mobile sensors
Computers & Geosciences
Evidence conflict analysis approach to obtain an optimal feature set for bayesian tutoring systems
ICICA'12 Proceedings of the Third international conference on Information Computing and Applications
Improving diagnosis agents with hybrid hypotheses confirmation reasoning techniques
EUMAS'11 Proceedings of the 9th European conference on Multi-Agent Systems
An investigation of critical factors in medical device development through Bayesian networks
Expert Systems with Applications: An International Journal
Detecting students' perception style by using games
Computers & Education
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Probabilistic networks, also known as Bayesian networks and influence diagrams, have become one of the most promising technologies in the area of applied artificial intelligence, offering intuitive, efficient, and reliable methods for diagnosis, prediction, decision making, classification, troubleshooting, and data mining under uncertainty. Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. Intended primarily for practitioners, this book does not require sophisticated mathematical skills or deep understanding of the underlying theory and methods nor does it discuss alternative technologies for reasoning under uncertainty. The theory and methods presented are illustrated through more than 140 examples, and exercises are included for the reader to check his/her level of understanding. The techniques and methods presented for knowledge elicitation, model construction and verification, modeling techniques and tricks, learning models from data, and analyses of models have all been developed and refined on the basis of numerous courses that the authors have held for practitioners worldwide.