Exploring the Intersection of Statistical Estimation Theory and Machine Learning

Dear Colleagues,

Machine learning methods in their various incarnations have become ubiquitous in nearly every branch of mathematical sciences, engineering, and even popular culture. These methods are deeply tied to the theory of random variables, and many techniques from statistical estimation theory have informed the growth and development of machine learning. Concepts such as fundamental mathematical statistics, Bayesian estimation theory, and information geometry have obvious and very intimate ties to the field of machine learning. The reverse is also true, with the development of tools such as Bayesian networks, deep Gaussian processes, and probabilistic programming making many of the rich results of statistical estimation theory feasibly applicable to an ever growing range of practical tasks. In this issue, we seek to solicit articles exploring the relationship between the two fields, with a special emphasis on techniques that lend rigor, insight, and depth to the often semi-empirical field of machine learning.

We also welcome papers with an applied focus that combine statistical estimation techniques with ML to solve new and interesting problems in domain-specific applications.

Dr. Jason M. Hite
Guest Editor

Manuscript Submission Information

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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. Mathematics 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 2600 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.

• estimation theory
• machine learning
• statistics