Hailfinder: A Bayesian system for forecasting severe weather

By B. Abramson, John M. Brown, W. Edwards, A. H. Murphy, Robert L. Winkler. 1996

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Citations in the corpus

Learning Neural Causal Models from Unknown Interventions (2019, background): “Causal structure learning using SCMs has been attempted in several disciplines including biology [2, 3], weather forecasting [4] and medicine [5, 6].”
A Bayesian network of eutrophication models for synthesis, prediction, and uncertainty analysis (2004, background): “The finding that only days with an oxygen concentration less than 2 mg/l have the potential for a fish kill suggests the use of a “collector node” to represent the scenarios of concern (Abramson et al., 1996).”
The ‘heuristics and biases’ bias in expert elicitation (2007, background): “Predicting extreme weather patterns is notoriously difficult (Abramson et al., 1996).)”
Learning Bayesian networks: approaches and issues (2011, background): “Many studies (probably wrongly) have assumed that Bayesian networks and causal Bayesian networks are equivalent (Acid & de Campos, 1995; Acid et al., 2001); more careful studies set out their assumptions clearly beforehand.”
Discovery of Causal Models that Contain Latent Variables through Bayesian Scoring of Independence Constraints (2017, methodology): “In particular, we simulated data from the Alarm [3], Hailfinder [1], and Hepar II [27] CBNs, which we obtained from [4].”
A Scoring Function for Learning Bayesian Networks based on Mutual Information and Conditional Independence Tests (2006, background): “Hailfinder (Abramson et al., 1996) is a normative system that forecasts severe summer hail in northeastern Colorado.”
Algorithms for Large Scale Markov Blanket Discovery (2003, methodology): “1989), which captures the structure of a medical domain having 37 variables, and on Hailfinder, a BN used for modeling and predicting the weather, published in (Abramson et al. 1996), with 56 variables.”