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BayesiaLab

The BayesiaLab Digest - April 17, 2015: Midway Revisited: Detecting Deception by Analysis of Competing Hypotheses

Here is today's citation of interesting applied research with Bayesian networks:

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The BayesiaLab Digest - February 9, 2015
Asymmetric Threat Detection with Bayesian Networks

Here is today's citation of new and interesting applied research with Bayesian networks:

Dragos, Valentina, Juergen Ziegler, and Paulo C. Costa.
Description and Assessment of a User Oriented Approach for Asymmetric Threat Detection.
Technical Report. George Mason University, 2013. 
http://oai.dtic.mil/oai/oai?verb=getRecord&metadataPrefix=html&identifier=ADA606212.

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The BayesiaLab Digest - January 26, 2015:
Modeling Aircraft Encounters with Bayesian Networks

Here is today's citation of new and interesting applied research with Bayesian networks:

Griffith, J. D., Matthew W. Edwards, Raymond M. Miraflor, and Andrew Weinert.
Due Regard Encounter Model Version 1.0.
Project Report. Lexington, Massachusetts: Massachusetts Institute of Technology Lincoln Laboratory, August 19, 2013.
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The BayesiaLab Digest - January 23, 2015:
Modelling crime linkage with Bayesian networks.

Here is today's citation of new and interesting applied research with Bayesian networks:

Jacob de Zoete, M.S., 2014.
Modelling crime linkage with Bayesian networks.

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The BayesiaLab Digest - January 22, 2015:
Evaluating and Ranking Threats to the Long-Term Persistence of Polar Bears.

Here is today's citation of new and interesting applied research with Bayesian networks:

Atwood, T., Marcot, B., Douglas, D., Amstrup, S., Rode, K., Durner, G., Bromaghin, J., 2015.
Evaluating and Ranking Threats to the Long-Term Persistence of Polar Bears.

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New Paper: A psychological approach to learning causal networks

Zargoush, Manaf, Farrokh Alemi, Vinzenzo Esposito Vinzi, Jee Vang, and Raya Kheirbek. “A Psychological Approach to Learning Causal Networks.” Health Care Management Science 17, no. 2 (June 1, 2014): 194–201. doi:10.1007/s10729-013-9250-2.

Abstract

We examine the role of a common cognitive heuristic in unsupervised learning of Bayesian probability networks from data. Human beings perceive a larger association between causal than diagnostic relationships. This psychological principal can be used to orient the arcs within Bayesian networks by prohibiting the direction that is less predictive. 

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2017 BayesiaLab Conference