Local computations with probabilities on graphical structures and their application to expert systems

By Steffen L. Lauritzen, David J. Spiegelhalter. 1990

Subfields: Probabilistic & Bayesian Methods, Knowledge Graphs & Representation

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Citations in the corpus

Variational Inference: A Review for Statisticians (2016, methodology): “Variational message passing connects variational inference to the classical theories of graphical models and probabilistic inference (Pearl 1988; Lauritzen and Spiegelhalter 1988).”
Learning Neural Causal Models from Unknown Interventions (2019, methodology): “Causal structure learning using SCMs has been attempted in several disciplines including biology [2, 3], weather forecasting [4] and medicine [5, 6].”
Causal inference by using invariant prediction: identification and confidence intervals (2015, background): “…[cf. Dawid, 2000, Rubin, 2005]; or structural equation modelling [cf. Bollen, 1989, Robins et al., 2000, Pearl, 2009] and corresponding graphical modeling [cf. Lauritzen and Spiegelhalter, 1988, Greenland et al., 1999, Spirtes et al., 2000], where the book by Pearl [2009] provides a nice overview.”
Determinantal Point Processes for Machine Learning (2012, methodology): “Going forward we will assume that the factor graph is a tree, since any low-treewidth factor graph can be converted into an equivalent factor tree with bounded factors using the junction tree algorithm [89].”
Learning Disentangled Representations with Semi-Supervised Deep Generative Models (2017, background): “At one end are fully-specified probabilistic graphical models [19, 22], in which a practitioner decides on all latent variables present in the joint distribution, the relationships between them, and the functional form of the conditional distributions which define the model.”
Optimal control as a graphical model inference problem (2009, positive): “In this case, we can find the exact solution via the junction tree (JT) algorithm (Lauritzen and Spiegelhalter 1988; Mooij 2010).”
Anchor regression: Heterogeneous data meet causality (2018, background): “ various types of assumptions and dierent frameworks, with applications ranging from public health to biology and economics [Dawid, 2000, Rubin, 2005, Bollen, 1989, Robins et al., 2000, Pearl, 2009, Lauritzen and Spiegelhalter, 1988, Greenland et al., 1999, Spirtes et al., 2000, Peters et al., 2017]”
Tractable Inference for Complex Stochastic Processes (1998, background): “tree [13] in which, for everyl, some clique contains X (t)”
Learning Dynamic Bayesian Networks (1997, unknown): “For multiply connected networks, in which there can be more than one undirected pa th between any two nodes, there exists a more general algorithm known as the junction tree algorithm [33, 25].”
A kernel-based causal learning algorithm (2007, background): “This network was .rst introduced by Lauritzen and Spiegelhalter (1988) who have speci.ed reasonable transition properties for each variable given its parents.”
The BUGS project: Evolution, critique and future directions (2009, background): “[33], and exact means of propagating uncertainty in tree structures [34] and general networks [35] were developed.”
From certainty factors to belief networks (1992, background): “Lauritzen and Spiegelhalter have created an algorithm that first builds an undirected graph from the belief network [26].”