Over the past decade, the 19th century science-of-counting was resurrected to provide a combinatorial derivation of conventional Machine Learning that uniquely generalizes statistics to probability theory, allows energy to enter or exit the system, and processes any time-series to return a complete set of scientific (thermodynamic) measurements as deductive reality [1].
Conventional machine learning is effective in static, closed world applications, but does not accommodate changing environments, where energy can enter or exit,...