Hailfinder

Hailfinder: A Bayesian system for forecasting severe weather. Hailfinder is a Bayesian system that combines meteorological data and model with expert judgment, based on both experience and physical understanding, to forecast severe weather in Northeastern Colorado. The system is based on a model, known as a belief network (BN), that has recently emerged as the basis of some powerful intelligent systems. Hailfinder is the first such system to apply these Bayesian models in the realm of meteorology, a field that has served as the basis of many past investigations of probabilistic forecasting. The design of Hailfinder provides a variety of insights to designers of other BN-based systems, regardless of their fields of application.


References in zbMATH (referenced in 20 articles )

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  1. Javidian, Mohammad Ali; Valtorta, Marco; Jamshidi, Pooyan: AMP chain graphs: minimal separators and structure learning algorithms (2020)
  2. Kim, Gang-Hoo; Kim, Sung-Ho: Marginal information for structure learning (2020)
  3. Topuz, Kazim; Uner, Hasmet; Oztekin, Asil; Yildirim, Mehmet Bayram: Predicting pediatric clinic no-shows: a decision analytic framework using elastic net and Bayesian belief network (2018)
  4. Tsirlis, Konstantinos; Lagani, Vincenzo; Triantafillou, Sofia; Tsamardinos, Ioannis: On scoring maximal ancestral graphs with the max-min hill climbing algorithm (2018)
  5. Isozaki, Takashi; Kuroki, Manabu: Learning causal graphs with latent confounders in weak faithfulness violations (2017)
  6. Mauá, Denis Deratani; Cozman, Fabio Gagliardi: Fast local search methods for solving limited memory influence diagrams (2016)
  7. Wang, Changzhang; Zhou, You; Zhao, Qiang; Geng, Zhi: Discovering and orienting the edges connected to a target variable in a DAG via a sequential local learning approach (2014)
  8. Acid, Silvia; De Campos, Luis M.; Fernández, Moisés: Score-based methods for learning Markov boundaries by searching in constrained spaces (2013)
  9. Alonso-Barba, Juan I.; delaOssa, Luis; Gámez, Jose A.; Puerta, Jose M.: Scaling up the greedy equivalence search algorithm by constraining the search space of equivalence classes (2013)
  10. Wang, Changzhang; Zhou, You; Geng, Zhi: Discovering causes and effects of a given node in Bayesian networks (2013)
  11. Isozaki, Takashi: Learning causal Bayesian networks using minimum free energy principle (2012)
  12. Alonso-Barba, Juan I.; de la Ossa, Luis; Gámez, Jose A.; Puerta, Jose M.: Scaling up the greedy equivalence search algorithm by constraining the search space of equivalence classes (2011)
  13. Butz, C. J.; Konkel, K.; Lingras, P.: Join tree propagation utilizing both arc reversal and variable elimination (2011) ioport
  14. Xu, Ping-Feng; Guo, Jianhua; Tang, Man-Lai: Structural learning for Bayesian networks by testing complete separators in prime blocks (2011)
  15. Butz, C. J.; Hua, S.; Konkel, K.; Yao, H.: Join tree propagation with prioritized messages (2010)
  16. Angelopoulos, Nicos; Cussens, James: Bayesian learning of Bayesian networks with informative priors (2008)
  17. Renooij, Silja; van der Gaag, Linda C.: Enhanced qualitative probabilistic networks for resolving trade-offs (2008)
  18. Van Allen, Tim; Singh, Ajit; Greiner, Russell; Hooper, Peter: Quantifying the uncertainty of a belief net response: Bayesian error-bars for belief net inference (2008)
  19. de Campos, Luis M.; Castellano, Javier G.: Bayesian network learning algorithms using structural restrictions (2007)
  20. Tsamardinos, Ioannis; Brown, Laura E.; Aliferis, Constantin F.: The max-min hill-climbing Bayesian network structure learning algorithm (2006)