Information graphs and their use for Bayesian network graph construction
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TLDR
The Information Graph (IG) formalism as discussed by the authors provides a precise account of the interplay between deductive and abductive inference and causal and evidential information, where "deduction" is used for defeasible "forward" inference.About:
This article is published in International Journal of Approximate Reasoning.The article was published on 2021-09-01 and is currently open access. It has received 1 citations till now. The article focuses on the topics: Abductive reasoning & Bayesian network.read more
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Bayesian networks in project management: A scoping review
TL;DR: In this article , a scoping review of the use of Bayesian Networks (BNs) in project management is presented, which highlights continued evolution of the literature on the subject, mainly in the last five years.
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MonographDOI
Causality: models, reasoning, and inference
TL;DR: The art and science of cause and effect have been studied in the social sciences for a long time as mentioned in this paper, see, e.g., the theory of inferred causation, causal diagrams and the identification of causal effects.
Book
Bayesian networks and decision graphs
TL;DR: The book introduces probabilistic graphical models and decision graphs, including Bayesian networks and influence diagrams, and presents a thorough introduction to state-of-the-art solution and analysis algorithms.
Journal ArticleDOI
On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n -person games
TL;DR: By showing that argumentation can be viewed as a special form of logic programming with negation as failure, this paper introduces a general logic-programming-based method for generating meta-interpreters for argumentation systems, a method very much similar to the compiler-compiler idea in conventional programming.
Journal ArticleDOI
A logic for default reasoning
TL;DR: This paper proposes a logic for default reasoning, develops a complete proof theory and shows how to interface it with a top down resolution theorem prover, and provides criteria under which the revision of derived beliefs must be effected.
Book
Modeling and Reasoning with Bayesian Networks
TL;DR: This book provides an extensive discussion of techniques for building Bayesian networks that model real-world situations, including techniques for synthesizing models from design, learning models from data, and debugging models using sensitivity analysis.