Hi, this is the author of the books which are being discussed in this thread and wanted to respond to some of the comments.
it_does_follow said "there is almost no mention of understanding parameter variance". I do discuss both Bayesian and frequentist measures of uncertainty in sec 4.6 and 4.7 of my intro book (http://probml.github.io/book1). (Ironically I included Cramer-Rao in an earlier draft, but omitted it from the final version due to space).
dxbydt said "how do you compute the variance of the sample variance". I discuss ways to estimate the (posterior) variance of a variance parameter in sec 3.3.3 of my advanced book (https://probml.github.io/pml-book/book2). I also discuss hierarchical Bayes, shrinkage, etc.
However in ML (and especially DL) the models we use are usually unidentifiable, and the parameters have no meaning, so nobody cares about quantifying their uncertainty. Instead the focus is on predictive uncertainty (of observable outcomes, not latent parameters). I discuss this in more detail in the advanced book, as well as related topics like distribution shift, causality, etc.
Concerning the comments on epub, etc. My book is made with latex and compiled to pdf. This is the current standard for technical publications and is what MIT Press uses.
Concerning the comments on other books. The Elements of Statistical Learning (Hastie, Tibhsirani, Friedman) and Pattern Recognition and ML (Bishop) are both great books, but are rather dated, and quite narrow in scope compared to my 2 volume collection....
Hi, I’m Kevin Murphy, one of the researchers at Google who worked on this project. Just to be clear, KV did NOT involve any private data sources -- it just analyzed public text on the web. (And yes, we do try to estimate reliability of the facts before incorporating them into KV.)
Also, KV is not a launched product, and is not replacing Knowledge Graph.
it_does_follow said "there is almost no mention of understanding parameter variance". I do discuss both Bayesian and frequentist measures of uncertainty in sec 4.6 and 4.7 of my intro book (http://probml.github.io/book1). (Ironically I included Cramer-Rao in an earlier draft, but omitted it from the final version due to space).
dxbydt said "how do you compute the variance of the sample variance". I discuss ways to estimate the (posterior) variance of a variance parameter in sec 3.3.3 of my advanced book (https://probml.github.io/pml-book/book2). I also discuss hierarchical Bayes, shrinkage, etc.
However in ML (and especially DL) the models we use are usually unidentifiable, and the parameters have no meaning, so nobody cares about quantifying their uncertainty. Instead the focus is on predictive uncertainty (of observable outcomes, not latent parameters). I discuss this in more detail in the advanced book, as well as related topics like distribution shift, causality, etc.
Concerning the comments on epub, etc. My book is made with latex and compiled to pdf. This is the current standard for technical publications and is what MIT Press uses.
Concerning the comments on other books. The Elements of Statistical Learning (Hastie, Tibhsirani, Friedman) and Pattern Recognition and ML (Bishop) are both great books, but are rather dated, and quite narrow in scope compared to my 2 volume collection....