Pierre-Simon Laplace in Th\u00e9orie Analytique Des Probabilit\u00e9s<\/p>\n
We need more decision \/ data scientists to ask “What is the probability of sun rising tomorrow?” yet we don’t seem to put a strong emphasis on probability theory for several reasons.<\/p>\n
Probability is not thoroughly covered (ideally as a standalone course) in most data science \/ business analytics programs. In predictive analytics, common packages\/libraries for ensemble methods focus on classifications, almost hiding the probability calculations (which are distorted in some anyway). Most frequentist reporting are limited to point estimates and errors, again hiding the underlying probabilistic assumptions. Et Cetera.<\/p>\n
In search of a short reading to share with my students, I’ve come across a recent book (updated in May, 2022) by Dirk P. Kroese, Zdravko Botev<\/a>, Thomas Taimre, Radislav (Slava) Vaisman Ph.D.<\/a> The book is open access and the appendices serve as a nice refresher. Appendix C is a little primer on probability for example.<\/p>\n