Probabilistic reasoning in intelligent systems: Networks of plausible inference

By A. Masood. 1991

Subfields: Probabilistic & Bayesian Methods

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Citations in the corpus

Gradient Estimation Using Stochastic Computation Graphs (2015, methodology): “) The notation and nomenclature we use is inspired by Bayes nets and influence diagrams [19].”
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).”
Logic meets Probability: Towards Explainable AI Systems for Uncertain Worlds (2017, methodology): “Modeling uncertain worlds needed a rigorous formulation, and this came in the form of probabilistic graphical models (PGMs) [Pearl, 1988].”
"Wrong side of the tracks": Big Data and Protected Categories (2014, background): “While both methods allow us to look inside previously opaque black boxes, they leave us uncertain of the causal mechanisms that led to this or that combination of variables being a good predictor of what we wish to know.”
Causality for Machine Learning (2019, positive): “While Judea’s approach arose out of classic AI, he was also one of the first to recognize some of the limitations of hard rules programmed by humans, and thus led the way in marrying classic AI with probability theory (Pearl, 1988).”
Discrete Variational Autoencoders (2016, methodology): “When a probabilistic model is defined in terms of a prior distribution p(z) and a conditional distribution p(x|z), the observation of x often induces strong correlations in the posterior p(z|x) due to phenomena such as explaining-away [36].”
Entailment, intensionality and text understanding (2003, background): “(Pearl, 1991), as well as attempting to use corpus based methods to detect entailment / subsumption, e.”
Column Networks for Collective Classification (2016, methodology): “This is a subject of AI with abundance of solutions including message passing algorithms (Pearl 1988), variational mean-ﬁeld (Opper and Saad 2001) and discrete optimization (Tran and Dinh Phung 2014).”
Human contribution to the record-breaking June and July 2019 heatwaves in Western Europe (2020, background): “The observations give a trend, but do not allow to attribute the trend to a cause in the traditional Pearl interpretation (Pearl 1988, Hannart et al 2016).”
Graph-based term weighting for information retrieval (2012, methodology): “…Doszkocs et al. 1990), and numerous approaches have been proposed to shift weights between vertices in the graphs (such as the Inference Network IR model Turtle and Croft 1991 based on the formalism of Bayesian networks Pearl 1988, or Logical Imaging formalisms Crestani and van Rijsbergen 1998).”
Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell Data (2005, background): “Bayesian networks ( 2 ), a form of graphical models, have been proffered as a promising framework for modeling complex systems such as cell signaling cascades, because they can represent probabilistic dependence relationships among multiple interacting components ( 3–5 ).”
Probabilistic Inductive Logic Programming (2004, methodology): “In this paper, we choose Bayesian logic programs [21] as the probabilistic logic programming system because Bayesian logic programs combine Bayesian networks [37], which represent probability distributions over propositional interpretations, with definite clause logic.”
An Introduction to Hidden Markov Models and Bayesian Networks (2001, unknown): “,X (M) t , St = m) = (2π)− D 2 |R|− 12 exp { −(1) 2 (Yt − C(m)X t )R(Y1 − CX (m) t ) } (44)”
Protein Molecular Function Prediction by Bayesian Phylogenomics (2005, background): “This functional form is known as a ‘‘noisy-OR’’ function [70], and it has the following interpretation.”
Bayesian Compressive Sensing Via Belief Propagation (2008, background): “Although our emphasis is on noiseless measurement of approximately sparse signals, we brieﬂy discuss noisy measurement of a strictly sparse signal, and show that a constant row weight L ensures that the measurements are approximated by two-state mixture Gaussians.”
An Introduction to Variational Methods for Graphical Models (1999, methodology): “These cross product terms couple the diseases (they are responsible for the " explaining away " phenomena that arise for the noisy- OR model; see Pearl, 1988).”