@dpwiz somehow none of this makes any sense to me -- I only know what an abstract PDF is (a measure), a few examples (uniform, normal, log.normal, Poisson, etc.), and the usual concepts coming with it: mean and the higher moments (becoming tensors in multivariate PDFs), and CDF with their median and quartiles (one dimension), also how to combine probabilities of individual events according to the logical definitions in terms of the connectives (conditional probability, simultaneous, independence, and Bayes theorem included)
once upon a time I also wrote a program for least-square optimization (ERP-like)
I don't know anything about things like maximum likelihood estimation or chi-squared test, or how to apply anything I know from PT in practice

generally sophisticated statistical estimates feel like free money -- trying to get more precision from the same information -- well you'd need actual data for it to be valuable, and the estimates are just that: adding sophisticated theory on top of guesses doesn't make the predictions any more accurate

I might need the tools like Squiggle at some point, but I'd prefer them to be on-premise and not a webapp, and also if learning them I need to start from the very beginning, from the very basic practical examples