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Bayesian Networks for Managing Learner Models in Adaptive Hypermedia Systems

knowing anything about the learner who uses the system, a system would behave in exactly the same way for all learners. Bayesian Networks for;

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Adaptive Hypermedia and Adaptive Web-Based Systems

Adaptive Hypermedia has emerged as an important area of both academic and deployed research. It encompasses a broad range of research that;

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Design Solutions for Adaptive Hypermedia Listening Software

pedagogically sound and efficient interactive adaptive hypermedia listening software for language learners in any language. The chapters will cover;

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Development of Clinical Decision Support Systems using Bayesian Networks

For the development of clinical decision support systems based on Bayesian networks, Mario A. Cypko investigates comprehensive expert;

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Bayesian Prediction and Adaptive Sampling Algorithms for Mobile Sensor Networks

of variability involved. The fully Bayesian approach also allows the most appropriate values for additional model parameters to be selected automatically;

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Hypermedia Genes

of information design methods. Hypermedia systems also became an ideal space for collaboration and cooperative work. Information access and sharing, and;

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Bayesian Networks for Reliability Engineering

This book presents a bibliographical review of the use of Bayesian networks in reliability over the last decade. Bayesian network (BN;

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Benefits of Bayesian Network Models

The application of Bayesian Networks (BN) or Dynamic Bayesian Networks (DBN) in dependability and risk analysis is a recent development. A;

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Modeling and Reasoning with Bayesian Networks

discussion of techniques for building Bayesian networks that model real-world situations, including techniques for synthesizing models from design;

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Modeling and Reasoning with Bayesian Networks

discussion of techniques for building Bayesian networks that model real-world situations, including techniques for synthesizing models from design;

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Advanced Methodologies for Bayesian Networks

This volume constitutes the refereed proceedings of the Second International Workshop on Advanced Methodologies for Bayesian Networks;

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Risk Assessment and Decision Analysis with Bayesian Networks

Although many Bayesian Network (BN) applications are now in everyday use, BNs have not yet achieved mainstream penetration. Focusing on;

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System Identification and Adaptive Control

models are suitable for partially-known or unknown complex time-varying systems. Neurofuzzy Adaptive Control contains rigorous proofs of its;

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Risk Assessment and Decision Analysis with Bayesian Networks

Since the first edition of this book published, Bayesian networks have become even more important for applications in a vast array;

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Personalization and Collaboration in Adaptive E-Learning

learning material. The adaptive term refers to the automatic adaptation of the system to the learner. The needs of the learner are borne by the;

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Personalization and Collaboration in Adaptive E-Learning

learning material. The adaptive term refers to the automatic adaptation of the system to the learner. The needs of the learner are borne by the;

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Probabilistic Graphical Models

. The book covers the fundamentals for each of the main classes of PGMs, including representation, inference and learning principles, and;

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Network-Oriented Modeling for Adaptive Networks

networks are networks that change their structure; for example, connections in Mental Networks usually change due to learning, while connections;

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Network-Oriented Modeling for Adaptive Networks

networks are networks that change their structure; for example, connections in Mental Networks usually change due to learning, while connections;

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Bayesian Networks and Decision Graphs

This is a brand new edition of an essential work on Bayesian networks and decision graphs. It is an introduction to probabilistic graphical;

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Bayesian Networks and Influence Diagrams

Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis, Second Edition, provides a comprehensive guide for;

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Bayesian Artificial Intelligence

. Adopting a causal interpretation of Bayesian networks, the authors discuss the use of Bayesian networks for causal modeling. They also draw on;

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Bayesian Artificial Intelligence

. Adopting a causal interpretation of Bayesian networks, the authors discuss the use of Bayesian networks for causal modeling. They also draw on;

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Machine Learning

possible, making the text suitable for different courses: pattern recognition, statistical/adaptive signal processing, statisticalBayesian;

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Uncertainty And Intelligent Information Systems

issues in uncertainty, the rapidly emerging discipline of information aggregation, neural networks, Bayesian networks and other network methods;

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Probabilistic Graphical Models

models, causal discovery and deep learning, as well as an even greater number of exercises; it also incorporates a software library for several;

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Probabilistic Graphical Models

models, causal discovery and deep learning, as well as an even greater number of exercises; it also incorporates a software library for several;

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