  This paper seeks to provide a thorough account of the ubiquitous nature of
the Bayesian paradigm in modern statistics, data science and artificial
intelligence. Once maligned, on the one hand by those who philosophically hated
the very idea of subjective probability used in prior specification, and on the
other hand because of the intractability of the computations needed for
Bayesian estimation and inference, the Bayesian school of thought now permeates
and pervades virtually all areas of science, applied science, engineering,
social science and even liberal arts, often in unsuspected ways. Thanks in part
to the availability of powerful computing resources, but also to the literally
unavoidable inherent presence of the quintessential building blocks of the
Bayesian paradigm in all walks of life, the Bayesian way of handling
statistical learning, estimation and inference is not only mainstream but also
becoming the most central approach to learning from the data. This paper
explores some of the most relevant elements to help to the reader appreciate
the pervading power and presence of the Bayesian paradigm in statistics,
artificial intelligence and data science, with an emphasis on how the Gospel
according to Reverend Thomas Bayes has turned out to be the truly good news,
and some cases the amazing saving grace, for all who seek to learn
statistically from the data. To further help the reader gain deeper and
tangible practical insights into the Bayesian machinery, we point to some
computational tools designed for the R Statistical Software Environment to help
explore Bayesian statistical learning.
