Explanation in Bayesian belief networks

By H. J. Suermondt. 1992

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Citations in the corpus

Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI (2019, neutral): “…and others, 2015; Liang and Van den Broeck, 2017; Nugent, Doyle, and Cunningham, 2009; Papadimitiou, Symeonidis and Manolopoulos, 2012; Pearl, 1988; Suermondt, 1992; Shafto and Goodman, 2008; Shafto, Goodman, and Griffiths, 2014; Sinha and Swearingen, 2002; Sørmo et al., 2005; Theis, Oord, and…”
How to Explain Individual Classification Decisions (2009, background): “For example, Suermondt (1992) measures the cost of omitting a single feature E i by the cross-entropy , where E denotes evidences and D = ( d 1 , . . . , d N ) T is the target variable.”
An evaluation of explanations of probabilistic inference. (1992, unknown): “For more details about INSITE, see (Suermondt, 1992).”
Interacting meaningfully with machine learning systems: Three experiments (2009, background): “These computationally expensive explanation algorithms for Bayesian networks consist of finding the subset of evidence that most influences the posterior probability and visualizing the flow of evidence through the network during inference (Suermondt, 1992; Chajewska and Draper, 1998; Lacave and Die”
Explanation in Probabilistic Systems: Is It Feasible? Will It Work? (2001, background): “Suermondt [26] provides a thorough quantitative treatment of several issues that are complementary to the QBP explanations.”
BANTER: a Bayesian network tutoring shell (1997, methodology): “Our approach to explanation generation is based on Suermondt’s [19] INSITE method, according to which explanation generation consists of two procedures.”
Qualitative Verbal Explanations in Bayesian Belief Networks (1996, background): “Of verbal explanations, two types addressed large models (as opposed to local interactions): scenario-based approach [4, 11] and belief propagation-based approach [11, 21].”
Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis (2007, background): “Further information on hypothesis-driven conflict analysis can be found in Suermondt (1992).”
A review of explanation methods for Bayesian networks (2002, methodology): “The purpose of INSITE is the comprehension of reasoning and it can be applied to both causal and non-causal networks.”
Bayesian Artificial Intelligence (2004, methodology): “They have been shown to support the detection of possible errors in the model [97] and can be used by the domain expert to verify the output of the model [271, 178, 77].”
Defining Explanation in Probabilistic Systems (1997, methodology): “…medical diagnosis systems, medical students not only trusted the system more when presented with an explanation of the diagnosis, but also were more confident about disagreeing with it when the explanations did not account adequately for all of the aspects of the case (Suennondt and Cooper 1992).”
Explaining inferences in Bayesian networks (2008, methodology): “Haddawy et al. [5] highlight that both BANTER and INSITE cannot detect influential inputs that interact among themselves, because their exhaustive approach requires an exponential number of network evaluations to do so.”
A two-phase method for extracting explanatory arguments from Bayesian networks (2017, methodology): “This resembles the work of Suermondt [15] although that does not apply argumentation, and the work of Schum [16] which is an informal approach to explaining Bayesian networks in argumentative30 terms.”