As pointed out elsewhere, typically people use “frequentist” to mean “non-Bayesian,” which is not particularly effective as a classification.
Reducing a frequentist model to a Bayesian one though it’s not a pointless excercise, since it elucidates the hidden assumptions, and at least you are better aware of its field of applicability.
Did you google Bayesian Machine Learning, or search for it on Amazon?
Only after buying the book I have :/
Bishop though seems a lot interesting, thanks!
The more meta point here is to not let a worldview shut you out from potentially useful resources.
Thankfully, I’m learning ML for my own education, it’s not something I need to practice right now.
You’re welcome! I should point out that the other words I was considering using to describe Bishop are “classic” and “venerable”—it’s not out of date (most actively used ML methods are surprisingly old), but you may want to read it in parallel with Barber. (In general, if you’ve never read textbooks in parallel before, I recommend it as a lesson in textbook design / pedagogy.)
Reducing a frequentist model to a Bayesian one though it’s not a pointless excercise, since it elucidates the hidden assumptions, and at least you are better aware of its field of applicability.
Only after buying the book I have :/ Bishop though seems a lot interesting, thanks!
Thankfully, I’m learning ML for my own education, it’s not something I need to practice right now.
You’re welcome! I should point out that the other words I was considering using to describe Bishop are “classic” and “venerable”—it’s not out of date (most actively used ML methods are surprisingly old), but you may want to read it in parallel with Barber. (In general, if you’ve never read textbooks in parallel before, I recommend it as a lesson in textbook design / pedagogy.)
Using Bishop in my class this Fall, very popular for good reason.