Probabilistic reasoning in intelligent systems: Networks of plausible inference

By S. K. Andersen. 1991

Subfields: Probabilistic & Bayesian Methods

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Citations in the corpus

DeepWalk: online learning of social representations (2014, methodology): “Techniques to address this problem typically use approximate inference techniques [31, 35] to leverage the dependency information to improve classification results.”
Fully Connected Deep Structured Networks (2015, background): “Otherwise computational complexity is prohibitively large and approximations like loopy belief propagation [26], convex belief propagation [39] or tree-reweighted message passing [37] are alternatives that were successfully applied.”
Stochastic variational inference (2012, methodology): “Statistical machine learning research has addressed some of these challenges by developing the ﬁeld of probabilistic modeling, a ﬁeld that provides an elegant approach to developing new methods for analyzing data (Pearl, 1988; Jordan, 1999; Bishop, 2006; Koller and Friedman, 2009; Murphy, 2012).”
Learning Deep Structured Models (2014, methodology): “In the future we plan to learn deeper models for object recognition in applications such as holistic semantic scene understanding.”
Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphology (2019, methodology): “Because our MRF is acyclic and tree-shaped, we can use belief propagation (Pearl, 1988) to perform exact inference.”
Kernel Mean Embedding of Distributions: A Review and Beyonds (2016, background): “…later for latent tree graphical models (Song et al. 2011b), which are often used for expressing hierarchical dependencies among many variables in computer vision and natural language processing, and for belief propagation algorithm (Pearl 1988, Song et al. 2011a) for pairwise Markov random fields.”
Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI (2019, background): “…Madigan, 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,…”
DeepProbLog: Neural Probabilistic Logic Programming (2018, background): “For instance, the following ProbLog program models a variant of the well-known alarm Bayesian network [16]:”
Causal Reasoning from Meta-reinforcement Learning (2019, background): “This section introduces the main definitions underlying this framework and explains how to visually test for statistical in-dependence (Pearl, 1988; Bishop, 2006; Koller and Friedman, 2009; Barber, 2012; Murphy, 2012).”
Relational inductive biases, deep learning, and graph networks (2018, methodology): “As an illustrative example of relational reasoning in machine learning, graphical models (Pearl, 1988; Koller and Friedman, 2009) can represent complex joint distributions by making explicit random conditional independences among random variables.”
Algorithmic independence of initial condition and dynamical law in thermodynamics and causal inference (2015, background): “Meanwhile, however, the increasing interdisciplinary field of causal inference has shown that also the latter is possible –even without information about time order– if appropriate assumptions that link causality and statistics are made [1, 2, 3], with applications in biology [4], psychology [5], an”
Kernel Belief Propagation (2011, methodology): “We begin with a short introduction to pairwise Markov random fields (MRFs) and the belief propagation algorithm.”
Deep Coordination Graphs (2019, methodology): “We compare DCG’s performance with that of other MARL Q -learning algorithms in a challenging family of predator-prey tasks that require coordinated actions.”
Normalizing Flows for Probabilistic Modeling and Inference (2019, background): “from the general framework of Bayesian networks (Pearl, 1988; Frey, 1998) to more recent neural-network-based implementations (Bengio and Bengio, 2000; Uria et al.”
Loopy Belief Propagation for Approximate Inference: An Empirical Study (1999, methodology): “7 in [12] investigates the quality of the ap proximation when it is applied to a particular loopy belief network.”
High-Dimensional Probability Estimation with Deep Density Models (2013, background): “Directed belief networks [Pearl, 1988, Neal, 1992, Adams et al., 2010], on the other hand, enable one to specify a priori marginals of hidden variables and are easily normalized, but require costly inference procedures.”
Efficient Solution Algorithms for Factored MDPs (2003, methodology): “We briefly review the MDP framework, referring the reader to the books by Bertsekas and Tsitsiklis (1996) or Puterman (1994) for a more in-depth review.”
Learning message-passing inference machines for structured prediction (2011, methodology): “Loopy Belief Propagation (BP) [16] is perhaps the canonical message-passing algorithm for performing (approximate) inference in graphical models.”
Deep Unfolding: Model-Based Inspiration of Novel Deep Architectures (2014, background): “Belief propagation (BP) is an algorithm for computing posterior probabilities, which leads to an exact solution for tree-structured graphical models [18].”