On the ubiquity of the Bayesian paradigm in statistical machine learning and data science
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This paper seeks to provide a thorough account of the ubiquitous natureof the Bayesian paradigm in modern statistics, data science and artificial intelli-gence. Once maligned, on the one hand by those who philosophically hated thevery idea of subjective probability used in prior specification, and on the otherhand because of the intractability of the computations needed for Bayesian esti-mation and inference, the Bayesian school of thought now permeates and pervadesvirtually all areas of science, applied science, engineering, social science and evenliberal arts, often in unsuspected ways. Thanks in part to the availability of pow-erful computing resources, but also to the literally unavoidable inherent presenceof the quintessential building blocks of the Bayesian paradigm in all walks of life,the Bayesian way of handling statistical learning, estimation and inference is notonly mainstream but also becoming the most central approach to learning from thedata. This paper explores some of the most relevant elements to help to the readerappreciate the pervading power and presence of the Bayesian paradigm in statistics,artificial intelligence and data science, with an emphasis on how the Gospel accord-ing to Reverend Thomas Bayes has turned out to be the truly good news, and insome cases the amazing saving grace, for all who seek to learn statistically from thedata.
Document typePeer reviewed
Document versionFinal PDF
SourceMathematics for Applications. 2019 vol. 8, č. 2, s. 189-209. ISSN 1805-3629
- 2019/2