Fusion, Propagation, and Structuring in Belief Networks

By Judea Pearl. 1986

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Citations in the corpus

Relational inductive biases, deep learning, and graph networks (2018, neutral): “…approaches, including logic, grammars, classic planning, graphical models, causal reasoning, Bayesian nonparametrics, and probabilistic programming (Chomsky, 1957; Nilsson and Fikes, 1970; Pearl, 1986, 2009; Russell and Norvig, 2009; Hjort et al., 2010; Goodman et al., 2012; Ghahramani, 2015).”
Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling (2018, background): “ which could lead to under-exploration. Expectation-Propagation The family of expectation-propagation algorithms (Opper & Winther, 2000; Minka, 2001b;a) is based on the message passing framework (Pearl, 1986). They iteratively approximate the posterior by updating a single approximation factor (”
Local computations with probabilities on graphical structures and their application to expert systems (1990, methodology): “Pearl (1986c) has shown how this can be handled probabilistically, and our allowance of logical links appears to circumvent theoretical problems.”
BeliefBank: Adding Memory to a Pre-Trained Language Model for a Systematic Notion of Belief (2021, background): “(Genesereth and Nilsson, 1987; Moore, 1983), belief revision (De Kleer, 1986; Dechter and Dechter, 1988), and uncertainty (Pearl, 1986).”
Learning Bayesian decision analysis by doing: lessons from environmental and natural resources management (1999, methodology): “The problem has been approached both from the direction of the Dempster–Shafer Theory of Evidence (e.g. Gordon and Shortliffe, 1985; Shenoy and Shafer, 1986), and from Bayesian statistics (e.g. Pearl, 1986a,b, 1988).”
Building large-scale Bayesian networks (2000, background): “…when he remarked, Fragmented structures of causal organisations are constantly being assembled on the ¯y, as needed, from a stock of building blocks (Pearl, 1986) The use of idioms ®lls a crucial gap in the literature on engineering BN systems by helping to identify the semantics and graph…”
The Computational Complexity of Probabilistic Inference Using Bayesian Belief Networks (1990, background): “Probabilistic inference using some topological classes of belief networks has been resistant to any efficient algorithmic solution [20].”
Complete Identification Methods for the Causal Hierarchy (2008, background): “The precise way in which the flow of dependence can be blocked is defined by the notion of d-separation (Pearl, 1986; Verma, 1986; Pearl, 1988).”
Reasoning about beliefs and actions under computational resource constraints (1987, background): “An example of a potentially useful local reformulation is the use of tractable prototypical dependency structures, such as the noisy-OR structure [27].”
Connectionist expert systems (1988, background): “See [4], [5], [21], [39], and [ 30 ] for some of this work.”
Secure Multiterminal Source Coding With Side Information at the Eavesdropper (2011, methodology): “Such a technique for establishing conditional independence was introduced in [68] for Bayesian networks, and further generalized to various types of graphs [69].”
Causal Inference from Graphical Models (2001, methodology): “An alternative formulation of the global, directed Markov property was given by Pearl (1986a) with a formal treatment in Verma and Pearl (1990).”
Uncertainty Aware AI ML: Why and How (2018, methodology): “SBP extends the Belief Propagation (BP) inference method of Pearl (Pearl 1986).”