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Bayesian Statistics on Artificial Intelligence: Theory, Methods and Applications

This special issue belongs to the section “Computing and Artificial Intelligence“.

Special Issue Information

Dear Colleagues,

We are inviting submissions to the Special Issue on Bayesian Statistics on Artificial Intelligence: Theory, Methods and Applications. Bayesian statistics are based on Bayesian inference that consists of prior, likelihood, and posterior distributions. Using Bayesian inference, Bayesian learning represents the update of human beliefs about events as a probability distribution. Thus, Bayesian statistics is one of popular fields in artificial intelligence (AI). Bayesian neural networks and Bayesian deep learning are the results of Bayesian statistics applied to AI. We know that Bayesian statistics are making various contributions to more AI domains. So, in this Special Issue, we invite submissions on diverse methods and applications of Bayesian statistics on AI. We welcome not only theoretical studies on Bayesian statistics for artificial intelligence but also various applied studies.

Prof. Dr. Sunghae Jun
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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.

Keywords

  • Bayesian statisitcs for machine learning
  • Bayesian neural networks
  • Bayesian deep learning
  • cognitive artficial intelligence using Bayesian inference
  • Bayesian networks
  • regression models using Bayesian approaches
  • classification models using Bayesian approaches
  • reinforcement learning using Bayesian approaches
  • Bayesian mixture models for artificial intelligence
  • Markov Chain Monte Carlo (MCMC) for artificial intelligence
  • big data analysis and visualization
  • statistical models for Artificial Intelliegnce
  • patent big data analysis using statistics and machine learning
Graphical abstract

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Appl. Sci. - ISSN 2076-3417
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