Dear Colleagues,

We invite you to submit your research on using Bayesian Networks to understand and explain causal systems, and to inform decision making under conditions of uncertainty. The algorithms of interest include those embedded within systems using Bayesian Networks, those generating Bayesian Networks (e.g., causal discovery) and those applying Bayesian Networks in complex decision making and analysis, and include informal algorithms describing how humans may use Bayesian Networks for these purposes. New methods, analyses, and experimental results across this spectrum are most welcome. Problems addressed may range from fundamental or theoretical (e.g., What are causes and how should they be represented in a causal model?), to psychological (e.g., How can Bayesian Networks reduce susceptibility to causal fallacies?), to applied (e.g., How can we use causal Bayesian Networks to improve disease surveillance?).

Dr. Kevin B Korb
Dr. Steven Mascaro
Erik P. Nyberg
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• causal bayesian networks
• causal discovery
• machine learning of bayesian networks
• causal reasoning
• causal inference
• evidential reasoning
• causal modelling
• actual causation
• causal criteria
• type and token causation
• statistical and causal fallacies
• reasoning under uncertainty
• bayesian decision making
• prediction and explanation with bayesian networks
• causal explanation
• causal attribution
• bayesian network applications