Propagating uncertainty in bayesian networks by probabilistic logic sampling

By Max Henrion. 1986

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Citations in the corpus

Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View (2019, background): “Markov networks [18], [70] and Bayesian networks [19], [71] are the”
Probabilistic models with unknown objects (2006, methodology): “We focused on three sampling-based approximation algorithms: rejection sampling [Henrion, 1988], likelihood weighting [Fung and Chang, 1990; Shachter and Peot, 1990], and Markov chain Monte Carlo with a Metropolis-Hastings transition distribution [Metropolis et al.”
Bayesian Networks and Decision Graphs (2001, background): “Loopy belief propagation is rooted in a version of probability updating for singly connected DAGs through message passing presented by Kim and Pearl (1983). In (Pearl, 1986), cutset-conditioning was used to reduce propagation in multiply connected networks to propagation in singly connected networks”
Learning Bayesian networks: approaches and issues (2011, methodology): “2.9.1 Logic sampling One of the first techniques to use Monte Carlo methods was introduced by Henrion (1988).”
An introduction and survey of estimation of distribution algorithms (2011, background): “New candidate solutions are generated by sampling the probability distribution encoded by the built network using probabilistic logic sampling (Henrion, 1988).”
Learning Bayesian networks from data: An information-theory based approach (2002, methodology): “In all cases, we generated synthesized data sets from their underlying probabilistic models using a Monte Carlo technique, called probabilistic logic sampling [30].”