Bayesian Networks and Decision Graphs

By Finn Verner Jensen. Statistics for Engineering and Information Science, 2001

5,093 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/2aef78ad47756436deecf6b3578d65dc37a61753

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

Citations in the corpus

A novel deep learning approach for classification of EEG motor imagery signals (2017, methodology): “In the classification part, many traditional algorithms like support vector machine (SVM) [2, 16], linear discriminant analysis (LDA) [17] and Bayesian classifier [18] have been employed in different studies [8, 15, 19].”
NETWORK INTRUSION DETECTION USING NAÏVE BAYES (2007, methodology): “Thus, a Bayesian network is used to model a domain containing uncertainty {12, 13].”
Bioinformatics Original Paper Integrating Multi-attribute Similarity Networks for Robust Representation of the Protein Space (neutral): “To address this issue, we use Bayesian correction (Jensen, 2001 ): a global prior is computed over all the bins, and later this is modified by the training data and normalized.”
Anomalous system call detection (2006, methodology): “That is, C is de.ned as follows. sc is normal : k 1 asi =I C(as1, as2, ..., ask, I) =ik =(8) sc is anomalous : 1 asi > I i= In our anomaly detection system, this simple summation scheme is replaced by a Bayesian network (for a good introduction to Bayesian networks, refer to [Jensen 2001]).”
Probability Theory and Causation: A Branching Space-Times Analysis (2005, background): “The framework of Bayesian Networks (Pearl, 2000; Jensen, 2001) has been especially prominent in this respect, and many important applications have been based on that framework.”
Assessing the decline of brown trout (Salmo trutta) in Swiss rivers using a Bayesian probability network (2006, background): “Probability networks have the advantage of making causal assumptions explicit and facilitating uncertainty analysis ( Pearl, 2000; Jensen, 2001 ).”
Discovering causal interactions using Bayesian network scoring and information gain (2016, background): “Bayesian networks [16,38 – 40] are an important architecture for reasoning under uncertainty in machine learning.”
Learning Bayesian networks: approaches and issues (2011, background): “…al. (2001) and Geiger (1998) look at different families of models (of which Bayesian networks are one) in the context of model selection, but a gentler introduction can be found in the pages of the books of Pearl (1988), Jensen and Nielsen (2007), Castillo et al. (1997a) and Cowell et al. (1999).”
THE EMPATHIC COMPANION: A CHARACTER-BASED INTERFACE THAT ADDRESSES USERS' AFFECTIVE STATES (2005, background): “A decision-theoretic agent selects actions that maximize the outcome in terms of some utility function (Jensen 2001).”
A two-phase method for extracting explanatory arguments from Bayesian networks (2017, background): “The second condition resembles the definition of barren nodes [29] in a Bayesian network except that nodes are barren iff they are uninstantiated and their children are barren.”
A Bayesian Belief Network modelling of organisational factors in risk analysis: A case study in maritime transportation (2008, background): “Scientific literature on risk analysis shows a recent diffusion of the use of BBN [15].”
Graphical model architectures for speech recognition (2005, background): “Moreover, it should be clear that it is the common factorization properties of the family of probability distributions that makes for efficient probabilistic inference [28], [29], [32], [41].”
Combinatorial Information Market Design (2003, positive): “This problem is often dealt with by creating a sparse dependence network, such as a Bayes net (Pearl, 1988; Jensen, 2001; Pennock and Wellman, 2000), relating the variables.”