Question to HN: is there somewhere a dictionary explaining and relating terms, in applicable way, that everyone uses without ever explaining them, such as "priors", "posteriors", "marginals", "belief", "marginal distribution", etc.
Regression and Other Stories by Andrew Gelman et al. is IMHO the best introduction to Bayesian reasoning without being overly technical, but still maintaining rigor and providing lots of insights about how statistics is done in practice.
if you're willing to sink time into a lecture series, you may enjoy Richard McElreath's introductory lectures on bayesian inference: https://www.youtube.com/watch?v=_NEMHM1wDfI
Comments
Question to HN: is there somewhere a dictionary explaining and relating terms, in applicable way, that everyone uses without ever explaining them, such as "priors", "posteriors", "marginals", "belief", "marginal distribution", etc.
Regression and Other Stories by Andrew Gelman et al. is IMHO the best introduction to Bayesian reasoning without being overly technical, but still maintaining rigor and providing lots of insights about how statistics is done in practice.
The first few chapters of many books about ML will go over these, like Pattern Recognition and Machine Learning by Bishop.
Try Bayesian Reasoning and Machine Learning specifically, as these are all about Bayesian reasoning.
if you're willing to sink time into a lecture series, you may enjoy Richard McElreath's introductory lectures on bayesian inference: https://www.youtube.com/watch?v=_NEMHM1wDfI